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 "online convex optimization for caching networks"):
  Abstract: We study the problem of wireless edge caching when file popularity is unknown and possibly non-stationary. A bank of $J$ caches receives file requests and a utility is accrued for each request depending on the serving cache. The network decides dynamically which files to store at each cache and how to route them, in order to maximize total utility. The request sequence is assumed to be drawn from an arbitrary distribution, thus capturing time-variance, temporal, or spatial locality of requests. For this challenging setting, we propose the \emph{Bipartite Supergradient Caching Algorithm} (BSCA) which provably exhibits no regret ($R_T/T \to 0$). That is, as the time horizon $T$ increases, BSCA achieves the same performance with the cache configuration that we would have chosen knowing all future requests. The learning rate of the algorithm is characterized by its regret expression, found to be $R_T=O(\sqrt{JT})$, which is independent of the content catalog size. For the single-cache case, we prove that this is the lowest attainable bound. BSCA requires at each step $J$ projections on intersections of boxes and simplices, for which we propose a tailored algorithm. Our model is the first that draws a connection between the network caching problem and Online Convex Optimization, and we demonstrate its generality by discussing various practical extensions and presenting a trace-driven comparison with state-of-the-art competitors.

Includes members:
  Member "learning to cache with no regrets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cacheable and non cacheable traffic interplay in a relay assisted wireless network")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "learning to cooperate in d2d caching networks":
    also connected through other hyperedges:
      (none)
  Member "cache optimization models and algorithms":
    also connected through other hyperedges:
      (none)
  Member "optimal and scalable caching for 5g using reinforcement learning of space time popularities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online learning with imperfect hints")
  Member "a reinforcement learning approach to proactive caching in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal pricing of internet of things a machine learning approach")
  Member "jointly optimal routing and caching for arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a ttl based approach for content placement in edge 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.NI(Networking and Internet Architecture)