human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "joint optimization of cloud and edge processing for fog radio access networks"):
  Abstract: This work studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This work focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.

Includes members:
  Member "fronthaul compression and precoding design for c rans over ergodic fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks")
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")
  Member "energy efficiency of downlink transmission strategies for cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse optimization for green edge ai inference")
  Member "cooperative caching and transmission design in cluster centric small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "fog networking an overview on research opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coded caching in fog ran b matching approach")
  Member "content centric sparse multicast beamforming for cache enabled cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")
  Member "cache aided wireless networks tradeoffs between storage and latency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "content caching and delivery in wireless radio access networks")

Question: Please tell me which class the source paper 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)