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 personalized preference learning framework for caching in mobile networks"):
  Abstract: This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learning these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose several greedy algorithms to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm along with the corresponding level of the approximation that it can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.

Includes members:
  Member "caching at base stations with heterogeneous user demands and spatial locality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal scheduling of content caching subject to deadline")
  Member "user preference learning based edge caching for fog radio access network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized asynchronous coded caching design and performance analysis in fog radio access networks")
  Member "on the effects of subpacketization in content centric mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the role of caching in future communication systems and networks")
  Member "how to cache in mobile hybrid iot networks":
    also connected through other hyperedges:
      (none)
  Member "caching policy for cache enabled d2d communications by learning user preference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative multi agent multi armed bandit learning for small cell caching")
  Member "pricing and resource allocation via game theory for a small cell video caching system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a crowdsourcing framework for on device federated learning")
  Member "information theoretic caching the multi user case":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery")
  Member "caching incentive design in wireless d2d networks a stackelberg game approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d2d enabled mobile user edge caching a multi winner auction approach")

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)