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: "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
  Abstract: The heterogeneous cloud radio access network (HCRAN) is a promising paradigm which integrates the advantages of cloud radio access network (C-RAN) and heterogeneous network (HetNet). In this paper, we study the joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed tradeoff between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems which can be solved concurrently at each slot. The third mixed-integer nonconvex subproblem is efficiently solved utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput-delay performance tradeoff with required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption.

Connected through hyperedge HE-1 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul"):
  with members:
    - "hybrid precoding based millimeter wave massive mimo noma with simultaneous wireless information and power transfer"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "safeguarding millimeter wave communications against randomly located eavesdroppers"
    - "energy efficient power allocation in millimeter wave massive mimo with non orthogonal multiple access"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "robust an aided beamforming and power splitting design for secure miso cognitive radio with swipt"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "secure communications in millimeter wave ad hoc networks"

Connected through hyperedge HE-2 (induced by source paper "joint beamforming and power splitting design for c ran with multicast fronthaul"):
  with members:
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "beamforming design for max min fair swipt in green cloud ran with wireless fronthaul"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "wireless powered communication opportunities and challenges"
    - "collaborative wireless energy and information transfer in interference channel"
    - "outage constrained robust transmit optimization for multiuser miso downlinks tractable approximations by conic optimization"

Connected through hyperedge HE-3 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-4 (induced by source paper "robust beamforming with pilot reuse scheduling in a heterogeneous cloud radio access network"):
  with members:
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "coordinated shared spectrum precoding with distributed csit"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"

Connected through hyperedge HE-5 (induced by source paper "resource allocation in cloud radio access networks with device to device communications"):
  with members:
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"

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)