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: "joint hybrid backhaul and access links design in cloud radio access networks"
  Abstract: The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base-station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed `1=`2 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).

Connected through hyperedge HE-1 (induced by source paper "computation offloading for iot in c ran optimization and deep learning"):
  with members:
    - "non stationarities in extra large scale massive mimo"
    - "cross layer design for mission critical iot in mobile edge computing systems"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "learning to optimize training deep neural networks for interference management"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-2 (induced by source paper "single or multiple frames content delivery for robust and secure communication in energy and spectrum efficient 5g networks"):
  with members:
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "fairness and transmission aware caching and delivery policies in ofdma based hetnets"
    - "a new look at physical layer security caching and wireless energy harvesting for heterogeneous ultra dense networks"
    - "limited feedback scheme for device to device communications in 5g cellular networks with reliability and cellular secrecy outage constraints"
    - "joint backhaul access analysis of full duplex self backhauling heterogeneous networks"
    - "green ofdma resource allocation in cache enabled cran"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "uplink contention based scma for 5g radio access"

Connected through hyperedge HE-3 (induced by source paper "robust beamforming in cache enabled cloud radio access networks"):
  with members:
    - "content centric multicast beamforming in cache enabled cloud radio access networks"
    - "what will 5g be"
    - "group sparse beamforming for green cloud ran"

Connected through hyperedge HE-4 (induced by source paper "energy efficient resource allocation for control data separation architecture based h cran with heterogeneous fronthaul"):
  with members:
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "5g wireless backhaul networks challenges and research advances"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"

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)