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 learning based approach to joint content caching and recommendation at base stations"):
  Abstract: Recommendation system is able to shape user demands, which can be used for boosting caching gain. In this paper, we jointly optimize content caching and recommendation at base stations. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific threshold. We then formulate a joint caching and recommendation problem maximizing the cache-hit ratio, which is NP-hard. To solve the problem efficiently, we develop a greedy algorithm. Since the user threshold is unknown in practice, we proceed to propose an $\epsilon$-greedy algorithm to learn the threshold via interactions with users. Simulation results show that the greedy algorithm achieves near-optimal performance and improves the cache-hit ratio significantly compared with priori works with/without recommendation. The $\epsilon$-greedy algorithm learns the user threshold quickly, and achieves more than $1-\epsilon$ of the cache-hit ratio obtained by the greedy algorithm with known user threshold.

Includes members:
  Member "use of deep learning in modern recommendation system a summary of recent works":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations")
  Member "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity")
  Member "femto caching with soft cache hits improving performance through recommendation and delivery of related content":
    also connected through other hyperedges:
      (none)
  Member "modeling analysis and optimization of coded caching in small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "caching at the wireless edge design aspects challenges and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks")
  Member "mixed timescale precoding and cache control in cached mimo interference network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint downlink scheduling for file placement and delivery in cache assisted wireless networks with finite file lifetime")
  Member "fundamental limits of caching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "multi antenna communication in ad hoc networks achieving mimo gains with simo transmission":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular 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)