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 "cache optimization models and algorithms"):
  Abstract: Storage resources and caching techniques permeate almost every area of communication networks today. In the near future, caching is set to play an important role in storage-assisted Internet architectures, information-centric networks, and wireless systems, reducing operating and capital expenditures and improving the services offered to users. In light of the remarkable data traffic growth and the increasing number of rich-media applications, the impact of caching is expected to become even more profound than it is today. Therefore, it is crucial to design these systems in an optimal fashion, ensuring the maximum possible performance and economic benefits from their deployment. To this end, this article presents a collection of detailed models and algorithms, which are synthesized to build a powerful analytical framework for caching optimization.

Includes members:
  Member "learning to cache with no regrets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online convex optimization for caching networks")
  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 "cabaret leveraging recommendation systems for mobile edge caching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey")
  Member "optimal and scalable caching for 5g using reinforcement learning of space time popularities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online convex optimization for caching networks")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online convex optimization for caching networks")
  Member "online convex optimization and no regret learning algorithms guarantees and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to cache with no regrets")
  Member "show me the cache optimizing cache friendly recommendations for sequential content access":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the order of things position aware network friendly recommendations in long viewing sessions")
  Member "a reinforcement learning approach to proactive caching in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online convex optimization for caching 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)