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: "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 What is more, simple popularity-based models commonly used (e.g. IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems.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.

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"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "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"
    - "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 "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"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "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-3 (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"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "making recommendations bandwidth aware"

Connected through hyperedge HE-4 (induced by source paper "the role of caching in future communication systems and networks"):
  with members:
    - "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "on coding for cache aided delivery of dynamic correlated content"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "updating content in cache aided coded multicast"
    - "on the effects of subpacketization in content centric mobile networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "cabaret leveraging recommendation systems for mobile edge caching"):
  with members:
    - "placing dynamic content in caches with small population"
    - "femtocaching wireless video content delivery through distributed caching helpers"

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)