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: "lru cache under stationary requests"
  Abstract: In this paper we focus on the LRU cache where requests for distinct contents are described by independent stationary and ergodic processes. We extend a TTL-based approximation of the cache hit probability first proposed by Fagin for the independence reference model to this more general workload model. We show that under very general conditions this approximation is exact as the cache size and the number of contents go to infinity. Moreover, we establish this not only for the aggregate cache hit probability but also for every individual content. Last, we obtain the rate of convergence.

Connected through hyperedge HE-1 (induced by source paper "what when and where to cache a unified optimization approach"):
  with members:
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "network cache design under stationary requests exact analysis and poisson approximation"
    - "accurate learning or fast mixing dynamic adaptability of caching algorithms"
    - "a utility optimization approach to network cache design"
    - "adaptive caching networks with optimality guarantees"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"
    - "a unified approach to the performance analysis of caching systems"

Connected through hyperedge HE-2 (induced by source paper "network cache design under stationary requests exact analysis and poisson approximation"):
  with members:
    - "on the convergence of the ttl approximation for an lru cache under independent stationary request processes"
    - "what when and where to cache a unified optimization approach"
    - "optimizing timer based policies for general cache networks"
    - "accurate learning or fast mixing dynamic adaptability of caching algorithms"
    - "a utility optimization approach to network cache design"
    - "a unified approach to the performance analysis of caching systems"
    - "impact of traffic mix on caching performance in a content centric network"

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.PF(Performance)