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: "a versatile and accurate approximation for lru cache performance"
  Abstract: In a 2002 paper, Che and co-authors proposed a simple approach for estimating the hit rates of a cache operating the least recently used (LRU) replacement policy. The approximation proves remarkably accurate and is applicable to quite general distributions of object popularity. This paper provides a mathematical explanation for the success of the approximation, notably in configurations where the intuitive arguments of Che, et al clearly do not apply. The approximation is particularly useful in evaluating the performance of current proposals for an information centric network where other approaches fail due to the very large populations of cacheable objects to be taken into account and to their complex popularity law, resulting from the mix of different content types and the filtering effect induced by the lower layers in a cache hierarchy.

Connected through hyperedge HE-1 (induced by source paper "online convex optimization for caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "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"

Connected through hyperedge HE-2 (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"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "learning to cache with no regrets"):
  with members:
    - "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"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-4 (induced by source paper "learning to cooperate in d2d caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "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"

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"
    - "learning to cache with no regrets"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "similarity caching theory and algorithms"):
  with members:
    - "a swiss army knife for dynamic caching in small cell networks"
    - "on the convergence of the ttl approximation for an lru cache under independent stationary request processes"
    - "linear time approximation schemes for geometric maximum coverage"
    - "clipper a low latency online prediction serving system"
    - "neural turing machines"

Connected through hyperedge HE-7 (induced by source paper "an analytical model for performance and lifetime estimation of hybrid dram nvm main memories"):
  with members:
    - "an operating system level data migration scheme in hybrid dram nvm memory architecture"
    - "eci cache a high endurance and cost efficient i o caching scheme for virtualized platforms"
    - "banshee bandwidth efficient dram caching via software hardware cooperation"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "a unified approach to the performance analysis of caching systems"

Connected through hyperedge HE-8 (induced by source paper "a caching system with object sharing"):
  with members:
    - "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)