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 "a reinforcement learning approach to proactive caching in wireless networks"):
  Abstract: We consider a mobile user accessing contents in a dynamic environment, where new contents are generated over time (by the user's contacts), and remain relevant to the user for random lifetimes. The user, equipped with a finite-capacity cache memory, randomly accesses the system, and requests all the relevant contents at the time of access. The system incurs an energy cost associated with the number of contents downloaded and the channel quality at that time. Assuming causal knowledge of the channel quality, the content profile, and the user-access behavior, we model the proactive caching problem as a Markov decision process with the goal of minimizing the long-term average energy cost. We first prove the optimality of a threshold-based proactive caching scheme, which dynamically caches or removes appropriate contents from the memory, prior to being requested by the user, depending on the channel state. The optimal threshold values depend on the system state, and hence, are computationally intractable. Therefore, we propose parametric representations for the threshold values, and use reinforcement-learning algorithms to find near-optimal parametrizations. We demonstrate through simulations that the proposed schemes significantly outperform classical reactive downloading, and perform very close to a genie-aided lower bound.

Includes members:
  Member "learning based caching in cloud aided wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative caching in fog radio access networks a graph based approach")
  Member "energy efficient wireless content delivery with proactive caching":
    also connected through other hyperedges:
      (none)
  Member "a deep reinforcement learning based framework for content caching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "joint pushing and caching with a finite receiver buffer optimal policies and throughput analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "delay analysis and optimization in cache enabled multi cell cooperative networks")
  Member "wireless content caching for small cell and d2d networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unified framework for constructing centralized coded caching schemes")
  Member "placing dynamic content in caches with small population":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online convex optimization for caching networks")
  Member "learning based optimization of cache content in a small cell base station":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal scheduling of content caching subject to deadline")

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.SY(Systems and Control)