human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "cabaret leveraging recommendation systems for mobile edge caching"
  Abstract: Joint caching and recommendation has been recently proposed for increasing the efficiency of mobile edge caching. While previous works assume collaboration between mobile network operators and content providers (who control the recommendation systems), this might be challenging in today's economic ecosystem, with existing protocols and architectures. In this paper, we propose an approach that enables cache-aware recommendations without requiring a network and content provider collaboration. We leverage information provided publicly by the recommendation system, and build a system that provides cache-friendly and high-quality recommendations. We apply our approach to the YouTube service, and conduct measurements on YouTube video recommendations and experiments with video requests, to evaluate the potential gains in the cache hit ratio. Finally, we analytically study the problem of caching optimization under our approach. Our results show that significant caching gains can be achieved in practice; 8 to 10 times increase in the cache hit ratio from cache-aware recommendations, and an extra 2 times increase from caching optimization.

Connected through hyperedge HE-1 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-2 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"
    - "towards mission critical control at the edge and over 5g"

Connected through hyperedge HE-3 (induced by source paper "the order of things position aware network friendly recommendations in long viewing sessions"):
  with members:
    - "a learning based approach to joint content caching and recommendation at base stations"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "wireless caching technical misconceptions and business barriers"
    - "soft cache hits and the impact of alternative content recommendations on mobile edge caching"
    - "making recommendations bandwidth aware"
    - "fundamental limits of caching in wireless d2d networks"
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-4 (induced by source paper "towards qos aware recommendations"):
  with members:
    - "should i stay or should i go analysis of the impact of application qos on user engagement in youtube"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "making recommendations bandwidth aware"

Question: Please tell me which class the center node 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)