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: "the minimal compression rate for similarity identification"
  Abstract: Traditionally, data compression deals with the problem of concisely representing a data source, e.g. a sequence of letters, for the purpose of eventual reproduction (either exact or approximate). In this work we are interested in the case where the goal is to answer similarity queries about the compressed sequence, i.e. to identify whether or not the original sequence is similar to a given query sequence. We study the fundamental tradeoff between the compression rate and the reliability of the queries performed on compressed data. For i.i.d. sequences, we characterize the minimal compression rate that allows query answers, that are reliable in the sense of having a vanishing false-positive probability, when false negatives are not allowed. The result is partially based on a previous work by Ahlswede et al., and the inherently typical subset lemma plays a key role in the converse proof. We then characterize the compression rate achievable by schemes that use lossy source codes as a building block, and show that such schemes are, in general, suboptimal. Finally, we tackle the problem of evaluating the minimal compression rate, by converting the problem to a sequence of convex programs that can be solved efficiently.

Connected through hyperedge HE-1 (induced by source paper "compression for quadratic similarity queries"):
  with members:
    - "compression for quadratic similarity queries finite blocklength and practical schemes"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-2 (induced by source paper "compression for quadratic similarity queries finite blocklength and practical schemes"):
  with members:
    - "compression for quadratic similarity queries"

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)