human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fractional programming for communication systems part i power control and beamforming"
  Abstract: Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave–convex FP problem—in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit"):
  with members:
    - "beam domain secure transmission for massive mimo communications"
    - "energy efficient multicell multigroup multicasting with joint beamforming and antenna selection"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "network deployment for maximal energy efficiency in uplink with multislope path loss"
    - "energy efficient beam coordination strategies with rate dependent processing power"
    - "bdma for millimeter wave terahertz massive mimo transmission with per beam synchronization"
    - "free deterministic equivalents for the analysis of mimo multiple access channel"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"

Connected through hyperedge HE-2 (induced by source paper "millimeter wave communications with an intelligent reflector performance optimization and distributional reinforcement learning"):
  with members:
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "reflections in the sky millimeter wave communication with uav carried intelligent reflectors"
    - "reflect and transmit array antennas for scalable and energy efficient mmwave massive mimo"
    - "intelligent reflecting and transmitting surface aided millimeter wave massive mimo"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"

Connected through hyperedge HE-3 (induced by source paper "coordinated passive beamforming for distributed intelligent reflecting surfaces network"):
  with members:
    - "over the air computation via intelligent reflecting surfaces"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "the roadmap to 6g ai empowered wireless networks"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "intelligent reflecting surface enhanced wireless network joint active and passive beamforming design"
    - "generalized sparse and low rank optimization for ultra dense networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-5 (induced by source paper "power efficient irs assisted noma"):
  with members:
    - "multi antenna covert communications in random wireless networks"
    - "resource allocation for secure irs assisted multiuser miso systems"
    - "beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts"
    - "intelligent reflecting surface assisted non orthogonal multiple access"
    - "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "a simple design of irs noma transmission"
    - "a speculative study on 6g"

Connected through hyperedge HE-6 (induced by source paper "twin timescale radio resource management for ultra reliable and low latency vehicular networks"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "physical layer authentication in mission critical mtc networks a security and delay performance analysis"
    - "a two stage allocation scheme for delay sensitive services in dense vehicular networks"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "low latency millimeter wave communications traffic dispersion or network densification"
    - "on the feasibility of wireless energy transfer using massive antenna arrays"
    - "blocklength limited performance of relaying under quasi static rayleigh channels"
    - "reliable and efficient autonomous driving the need for heterogeneous vehicular networks"

Connected through hyperedge HE-7 (induced by source paper "power allocation in multi user cellular networks deep reinforcement learning approaches"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "a machine learning approach for power allocation in hetnets considering qos"
    - "power allocation in multi user cellular networks with deep q learning approach"
    - "an introduction to deep learning for the physical layer"
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep reinforcement learning an overview"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-8 (induced by source paper "sum throughput maximization in multi tag backscattering to multiantenna reader"):
  with members:
    - "optimal channel estimation for reciprocity based backscattering with a full duplex mimo reader"
    - "cooperative ambient backscatter communications for green internet of things"
    - "retrodirective large antenna energy beamforming in backscatter multi user networks"
    - "transmit precoding and receive power splitting for harvested power maximization in mimo swipt systems"
    - "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers"
    - "inference from randomized transmissions by many backscatter sensors"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "multi antenna wireless energy transfer for backscatter communication systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)