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: "fog computing based radio access networks issues and challenges"
  Abstract: An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified.

Connected through hyperedge HE-1 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "joint user association and resource allocation in virtualized wireless networks"

Connected through hyperedge HE-2 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"

Connected through hyperedge HE-3 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks"):
  with members:
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "two level transmission scheme for cache enabled fog radio access networks"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "joint design of digital and analog processing for downlink c ran with large scale antenna arrays"
    - "codebook based hybrid precoding for millimeter wave multiuser systems"

Connected through hyperedge HE-5 (induced by source paper "cloud edge coordinated processing low latency multicasting transmission"):
  with members:
    - "hybrid precoder design for cache enabled millimeter wave radio access networks"
    - "two level transmission scheme for cache enabled fog radio access networks"
    - "iteratively linearized reweighted alternating direction method of multipliers for a class of nonconvex problems"
    - "content placement in cache enabled sub 6 ghz and millimeter wave multi antenna dense small cell networks"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "distributed optimization for coordinated beamforming in multicell multigroup multicast systems power minimization and sinr balancing"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "random caching based cooperative transmission in heterogeneous wireless networks"

Connected through hyperedge HE-6 (induced by source paper "fog aided device to device networks with opportunistic content delivery"):
  with members:
    - "resource allocation in noma based fog radio access networks"
    - "fog radio access networks mobility management interference mitigation and resource optimization"
    - "d2d aware device caching in mmwave cellular networks"
    - "spatially correlated content caching for device to device communications"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "the three primary colors of mobile systems"
    - "mobility aware caching in d2d networks"
    - "wireless content caching for small cell and d2d networks"

Connected through hyperedge HE-7 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  with members:
    - "start making sense semantic plane filtering and control for post 5g connectivity"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
    - "deep joint source channel coding for wireless image transmission"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a brief introduction to machine learning for engineers"

Connected through hyperedge HE-8 (induced by source paper "distributed edge caching via reinforcement learning in fog radio access networks"):
  with members:
    - "user preference learning based edge caching for fog radio access network"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "distributed edge caching in ultra dense fog radio access networks a mean field approach"
    - "ultra dense edge caching under spatio temporal demand and network dynamics"
    - "offloading content with self organizing mobile fogs"
    - "a cmdp based approach for energy efficient power allocation in massive mimo systems"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "placing dynamic content in caches with small population"

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)