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 "show me the cache optimizing cache friendly recommendations for sequential content access"):
  Abstract: Caching has been successfully applied in wired networks, in the context of Content Distribution Networks (CDNs), and is quickly gaining ground for wireless systems. Storing popular content at the edge of the network (e.g. at small cells) is seen as a `win-win' for both the user (reduced access latency) and the operator (reduced load on the transport network and core servers). Nevertheless, the much smaller size of such edge caches, and the volatility of user preferences suggest that standard caching methods do not suffice in this context. What is more, simple popularity-based models commonly used (e.g. IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems. The latter presents a great opportunity to bias the recommender to minimize content access cost (e.g. maximizing cache hit rates). To this end, in this paper we first propose a Markovian model for recommendation-driven user requests. We then formulate the problem of biasing the recommendation algorithm to minimize access cost, while maintaining acceptable recommendation quality. We show that the problem is non-convex, and propose an iterative ADMM-based algorithm that outperforms existing schemes, and shows significant potential for performance improvement on real content datasets.

Includes members:
  Member "a learning based approach to joint content caching and recommendation at base stations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the order of things position aware network friendly recommendations in long viewing sessions")
  Member "effects of content popularity on the performance of content centric opportunistic networking an analytical approach and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soft cache hits and the impact of alternative content recommendations on mobile edge caching")
  Member "collaborative multi bitrate video caching and processing in mobile edge computing networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge federation towards an integrated service provisioning model")
  Member "soft cache hits and the impact of alternative content recommendations on mobile edge caching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the order of things position aware network friendly recommendations in long viewing sessions")
  Member "placing dynamic content in caches with small population":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online convex optimization for caching networks")
  Member "finite length analysis of caching aided coded multicasting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "living on the edge the role of proactive caching in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "fundamental limits of caching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")

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)