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 novel centralized strategy for coded caching with non uniform demands"
  Abstract: Despite significant progress in the caching literature concerning the worst case and uniform average case regimes, the algorithms for caching with nonuniform demands are still at a basic stage and mostly rely on simple grouping and memory-sharing techniques. In this work we introduce a novel centralized caching strategy for caching with nonuniform file popularities. Our scheme allows for assigning more cache to the files which are more likely to be requested, while maintaining the same sub-packetization for all the files. As a result, in the delivery phase it is possible to perform linear codes across files with different popularities without resorting to zero-padding or concatenation techniques. We will describe our placement strategy for arbitrary range of parameters. The delivery phase will be outlined for a small example for which we are able to show a noticeable improvement over the state of the art.

Connected through hyperedge HE-1 (induced by source paper "the optimal memory rate trade off for the non uniform centralized caching problem with two files under uncoded placement"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "full characterization of optimal uncoded placement for the structured clique cover delivery of nonuniform demands"
    - "speeding up distributed machine learning using codes"
    - "uncoded caching and cross level coded delivery for non uniform file popularity"
    - "optimization of heterogeneous coded caching"
    - "characterizing the rate memory tradeoff in cache networks within a factor of 2"
    - "structural properties of uncoded placement optimization for coded delivery"
    - "fundamental limits of caching improved bounds with coded prefetching"

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.IT(Information Theory)