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 "reversing the meaning of node connectivity for content placement in networks of caches"):
  Abstract: It is a widely accepted heuristic in content caching to place the most popular content at the nodes that are the best connected. The other common heuristic is somewhat contradictory, as it places the most popular content at the edge, at the caching nodes nearest the users. We contend that neither policy is best suited for caching content in a network and propose a simple alternative that places the most popular content at the least connected node. Namely, we populate content first at the nodes that have the lowest graph centrality over the network topology. Here, we provide an analytical study of this policy over some simple topologies that are tractable, namely regular grids and trees. Our mathematical results demonstrate that placing popular content at the least connected nodes outperforms the aforementioned alternatives in typical conditions.

Includes members:
  Member "jointly optimal routing and caching for arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online convex optimization for caching networks")
  Member "maximizing coverage centrality via network design extended version":
    also connected through other hyperedges:
      (none)
  Member "adaptive caching networks with optimality guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a ttl based approach for content placement in edge networks")
  Member "fundamental limits on throughput capacity in information centric networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the capacity of fractal d2d social networks with hierarchical communications")

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)