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: "practical bounds on optimal caching with variable object sizes"
  Abstract: Many recent caching systems aim to improve hit ratios, but there is no good sense among practitioners of how much further hit ratios can be improved. In other words, should the systems community continue working on this problem? Currently, there is no principled answer to this question. Most prior work assumes that objects have the same size, but in practice object sizes often vary by several orders of magnitude. The few known results for variable object sizes provide very weak guarantees and are impractical to compute on traces of realistic length. We propose a new method to compute the offline optimal hit ratio under variable object sizes. Our key insight is to represent caching as a min-cost flow problem, hence we call our method the flow-based offline optimal (FOO). We show that, under simple independence assumptions and Zipf popularities, FOO's bounds become tight as the number of objects goes to infinity. From FOO we develop fast, practical methods to compute nearly tight bounds for the optimal hit ratio, which we call practical flow-based offline optimal (P-FOO). P-FOO enables the first analysis of optimal caching on realistic traces with hundreds of millions of requests. We evaluate P-FOO on several production traces, where results show that recent caching systems are still far from optimal.

Connected through hyperedge HE-1 (induced by source paper "worst case bounds and optimized cache on mth request cache insertion policies under elastic conditions"):
  with members:
    - "caching in the clouds optimized dynamic cache instantiation in content delivery systems"
    - "a utility optimization approach to network cache design"
    - "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.PF(Performance)