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: "learning to cache with no regrets"
  Abstract: This paper introduces a novel caching analysis that, contrary to prior work, makes no modeling assumptions for the file request sequence. We cast the caching problem in the framework of Online Linear Optimization (OLO), and introduce a class of minimum regret caching policies, which minimize the losses with respect to the best static configuration in hindsight when the request model is unknown. These policies are very important since they are robust to popularity deviations in the sense that they learn to adjust their caching decisions when the popularity model changes. We first prove a novel lower bound for the regret of any caching policy, improving existing OLO bounds for our setting. Then we show that the Online Gradient Ascent (OGA) policy guarantees a regret that matches the lower bound, hence it is universally optimal. Finally, we shift our attention to a network of caches arranged to form a bipartite graph, and show that the Bipartite Subgradient Algorithm (BSA) has no regret

Connected through hyperedge HE-1 (induced by source paper "online convex optimization for caching networks"):
  with members:
    - "introduction to online convex optimization"
    - "learning to cooperate in d2d caching networks"
    - "cache optimization models and algorithms"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "a low complexity approach to distributed cooperative caching with geographic constraints"

Connected through hyperedge HE-2 (induced by source paper "cacheable and non cacheable traffic interplay in a relay assisted wireless network"):
  with members:
    - "wireless caching technical misconceptions and business barriers"
    - "energy efficiency of downlink networks with caching at base stations"
    - "optimal caching placement for d2d assisted wireless caching networks"

Connected through hyperedge HE-3 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "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"
    - "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-4 (induced by source paper "learning to cooperate in d2d caching networks"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "a low complexity approach to distributed cooperative caching with geographic constraints"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "efficient and fair collaborative mobile internet access"

Connected through hyperedge HE-5 (induced by source paper "a swiss army knife for dynamic caching in small cell networks"):
  with members:
    - "similarity caching theory and algorithms"
    - "spatial multi lru caching for wireless networks with coverage overlaps"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"
    - "efficient analysis of caching strategies under dynamic content popularity"
    - "a unified approach to the performance analysis of caching systems"
    - "a versatile and accurate approximation for lru cache performance"

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)