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: "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
  Abstract: In a densely deployed heterogeneous network (HetNet), the number of pico/micro base stations (BS) can be comparable with the number of the users. To reduce the operational overhead of the HetNet, proper identification of the set of serving BSs becomes an important design issue. In this work, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide formulations and efficient algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs. We illustrate the efficiency and the efficacy of the proposed algorithms via extensive numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines"):
  with members:
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "base station on off switching in 5g wireless networks approaches and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "joint optimization of cloud and edge processing for fog radio access networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "large scale convex optimization for ultra dense cloud ran"

Connected through hyperedge HE-2 (induced by source paper "learning to optimize training deep neural networks for interference management"):
  with members:
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "why deep neural networks for function approximation"
    - "recurrent neural radio anomaly detection"
    - "signal processing and optimal resource allocation for the interference channel"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-3 (induced by source paper "drag deep reinforcement learning based base station activation in heterogeneous networks"):
  with members:
    - "parameter space noise for exploration"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep reinforcement learning in parameterized action space"
    - "deep reinforcement learning with double q learning"
    - "downlink performance of cellular systems with base station sleeping user association and scheduling"

Connected through hyperedge HE-4 (induced by source paper "a centralized metropolitan scale radio resource management scheme"):
  with members:
    - "hybrid spectrum sharing in mmwave cellular networks"
    - "the impact of unlicensed access on small cell resource allocation"
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-5 (induced by source paper "large scale spectrum allocation for cellular networks via sparse optimization"):
  with members:
    - "scalable spectrum allocation and user association in networks with many small cells"
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-6 (induced by source paper "enhanced group sparse beamforming for green cloud ran a random matrix approach"):
  with members:
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"

Connected through hyperedge HE-7 (induced by source paper "scalable spectrum allocation and user association in networks with many small cells"):
  with members:
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "traffic driven spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "on association cells in random heterogeneous networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-8 (induced by source paper "joint transmission with limited backhaul connectivity"):
  with members:
    - "distributed bi directional training of nonlinear precoders and receivers in cellular networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "adaptive spatial intercell interference cancellation in multicell wireless networks"
    - "pilot contamination and precoding in multi cell tdd 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)