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 "deep reinforcement learning based mode selection and resource management for green fog radio access networks"):
  Abstract: Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

Includes members:
  Member "learning to optimize training deep neural networks for interference management":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "on reducing iot service delay via fog offloading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling fog offloading performance")
  Member "layered group sparse beamforming for cache enabled green wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint fronthaul multicast and cooperative beamforming for cache enabled cloud based small cell networks an mds codes aided approach")
  Member "an evolutionary game for user access mode selection in fog radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evolutionary games for correlation aware clustering in massive machine to machine networks")
  Member "deep reinforcement learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low rank bottleneck in multi head attention models")
  Member "a deep reinforcement learning based framework for content caching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust beamforming with pilot reuse scheduling in a heterogeneous cloud radio access network")
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")

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.NI(Networking and Internet Architecture)