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 "using grouped linear prediction and accelerated reinforcement learning for online content caching"):
  Abstract: Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.

Includes members:
  Member "optimal and scalable caching for 5g using reinforcement learning of space time popularities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online convex optimization for caching networks")
  Member "dynamic mobile edge caching with location differentiation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "content popularity prediction towards location aware mobile edge caching")
  Member "context aware proactive content caching with service differentiation in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "a learning based approach to caching in heterogenous small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online convex optimization for caching networks")
  Member "continuous deep q learning with model based acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "a transfer learning approach for cache enabled wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  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")
  Member "content level selective offloading in heterogeneous networks multi armed bandit optimization and regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal and scalable caching for 5g using reinforcement learning of space time popularities")

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)