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 "multi tenant cross slice resource orchestration a deep reinforcement learning approach"):
  Abstract: In a software-defined radio access network (RAN), a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a software-defined RAN, where a limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from competition with other SPs for the orchestration of channel access opportunities over its MUs, which request both mobile-edge computing and traditional cellular services in the slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the network dynamics as well as the control policies of its competitors. We propose an abstract stochastic game to approximate the Nash equilibrium. The selfish behaviours of a SP can then be characterized by a single-agent Markov decision process (MDP). To simplify decision makings, we linearly decompose the per-SP MDP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

Includes members:
  Member "low complexity distributed radio access network slicing algorithms and experimental results":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency")
  Member "distributed resource allocation for network slicing over licensed and unlicensed bands":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed resource allocation for network slicing of bandwidth and computational resource")
  Member "dynamic network slicing for scalable fog computing systems with energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secrecy preserving in stochastic resource orchestration for multi tenancy network slicing")
  Member "performance optimization in mobile edge computing via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "network slicing for 5g with sdn nfv concepts architectures and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "near optimal behavior via approximate state abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning state abstractions for transfer in continuous control")
  Member "dynamic computation offloading for mobile edge computing with energy harvesting devices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neuromemristive circuits for edge computing a review")

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)