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 resource allocation optimization for multimedia heterogeneous cloud radio access networks"
  Abstract: The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "on reducing iot service delay via fog offloading"
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "an evolutionary game for user access mode selection in fog radio access networks"
    - "deep reinforcement learning an overview"
    - "a deep reinforcement learning based framework for content caching"
    - "fog computing based radio access networks issues and challenges"
    - "csi based fingerprinting for indoor localization a deep learning approach"

Connected through hyperedge HE-2 (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"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "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-3 (induced by source paper "recent advances in fog radio access networks performance analysis and radio resource allocation"):
  with members:
    - "fog computing based radio access networks issues 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"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "user access mode selection in fog computing based radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "contract based interference coordination in heterogeneous cloud radio access networks"

Connected through hyperedge HE-4 (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"
    - "fog computing based radio access networks issues and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "channel matrix sparsity with imperfect channel state information in cloud radio access networks"):
  with members:
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "training design and channel estimation in uplink cloud radio access networks"
    - "large system analysis of base station cooperation for power minimization"

Connected through hyperedge HE-6 (induced by source paper "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"):
  with members:
    - "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"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "energy efficient resource assignment and power allocation 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"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "resource allocation optimization for delay sensitive traffic in fronthaul constrained cloud radio access networks"

Connected through hyperedge HE-7 (induced by source paper "economical energy efficiency e3 an advanced performance metric for 5g systems"):
  with members:
    - "fog computing based radio access networks issues and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "joint precoding and multivariate backhaul compression for the downlink of 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.NI(Networking and Internet Architecture)