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: "an overview on resource allocation techniques for multi user mimo systems"
  Abstract: Remarkable research activities and major advances have been occurred over the past decade in multiuser multiple-input multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

Connected through hyperedge HE-1 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "symbol level and multicast precoding for multiuser multiantenna downlink a survey classification and challenges"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "joint beamforming and broadcasting in massive mimo"
    - "coordinate update algorithms for robust power loading for the mu miso downlink with outage constraints"
    - "energy efficient symbol level precoding in multiuser miso based on relaxed detection region"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "adaptive pilot clustering in heterogeneous massive mimo networks"
    - "a rate splitting strategy for max min fair multigroup multicasting"

Connected through hyperedge HE-3 (induced by source paper "rate splitting for max min fair multigroup multicast beamforming in overloaded systems"):
  with members:
    - "generalized multicast multibeam precoding for satellite communications"
    - "elevated multiplexing and signal space partitioning in the 2 user mimo ic with partial csit"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "a rate splitting strategy for max min fair multigroup multicasting"
    - "overloaded multiuser miso transmission with imperfect csit"
    - "rate splitting for mimo wireless networks a promising phy layer strategy for lte evolution"
    - "a minorization maximization method for optimizing sum rate in the downlink of non orthogonal multiple access systems"

Connected through hyperedge HE-4 (induced by source paper "a survey on qoe oriented wireless resources scheduling"):
  with members:
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "wiflix adaptive video streaming in massive mu mimo wireless networks"
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"

Connected through hyperedge HE-5 (induced by source paper "a machine learning framework for resource allocation assisted by cloud computing"):
  with members:
    - "wireless communications in the era of big data"

Connected through hyperedge HE-6 (induced by source paper "a new approach to user scheduling in massive multi user mimo broadcast channels"):
  with members:
    - "on the role of transmit correlation diversity in multiuser mimo systems"
    - "two stage beamformer design for massive mimo downlink by trace quotient formulation"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "random beamforming with heterogeneous users and selective feedback individual sum rate and individual scaling laws"
    - "joint spatial division and multiplexing opportunistic beamforming and user grouping"

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)