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: "layered group sparse beamforming for cache enabled green wireless networks"
  Abstract: The exponential growth of mobile data traffic is driving the deployment of dense wireless networks, which will not only impose heavy backhaul burdens, but also generate considerable power consumption. Introducing caches to the wireless network edge is a potential and cost-effective solution to address these challenges. In this paper, we will investigate the problem of minimizing the network power consumption of cache-enabled wireless networks, consisting of the base station (BS) and backhaul power consumption. The objective is to develop efficient algorithms that unify adaptive BS selection, backhaul content assignment and multicast beamforming, while taking account of user QoS requirements and backhaul capacity limitations. To address the NP-hardness of the network power minimization problem, we first propose a generalized layered group sparse beamforming (LGSBF) modeling framework, which helps to reveal the layered sparsity structure in the beamformers. By adopting the reweighted $\left.\ell_{1}\right/\ell_{2}$-norm technique, we further develop a convex approximation procedure for the LGSBF problem, followed by a three-stage iterative LGSBF framework to induce the desired sparsity structure in the beamformers. Simulation results validate the effectiveness of the proposed algorithm in reducing the network power consumption, and demonstrate that caching plays a more significant role in networks with higher user densities and less power-efficient backhaul links.

Connected through hyperedge HE-1 (induced by source paper "joint fronthaul multicast and cooperative beamforming for cache enabled cloud based small cell networks an mds codes aided approach"):
  with members:
    - "joint long term cache allocation and short term content delivery in green cloud small cell networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "joint optimization of user association data delivery rate and precoding for cache enabled f rans"
    - "optimizing mds coded caching in wireless networks with device to device communication"
    - "coded multicast fronthauling and edge caching for multi connectivity transmission in fog radio access networks"
    - "delivery time minimization in edge caching synergistic benefits of subspace alignment and zero forcing"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"

Connected through hyperedge HE-2 (induced by source paper "joint long term cache allocation and short term content delivery in green cloud small cell networks"):
  with members:
    - "cache enabled physical layer security for video streaming in backhaul limited cellular networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-3 (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"
    - "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"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"
    - "csi based fingerprinting for indoor localization a deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "enhanced group sparse beamforming for green cloud ran a random matrix approach"):
  with members:
    - "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"
    - "are we approaching the fundamental limits of wireless network densification"

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)