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: "euclidean information theory of networks"
  Abstract: In this paper, we extend the information theoretic framework that was developed in earlier work to multi-hop network settings. For a given network, we construct a novel deterministic model that quantifies the ability of the network in transmitting private and common messages across users. Based on this model, we formulate a linear optimization problem that explores the throughput of a multi-layer network, thereby offering the optimal strategy as to what kind of common messages should be generated in the network to maximize the throughput. With this deterministic model, we also investigate the role of feedback for multi-layer networks, from which we identify a variety of scenarios in which feedback can improve transmission efficiency. Our results provide fundamental guidelines as to how to coordinate cooperation between users to enable efficient information exchanges across them.

Connected through hyperedge HE-1 (induced by source paper "distributed hypothesis testing with privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "on hypothesis testing against independence with multiple decision centers"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "distributed hypothesis testing over noisy channels"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "utility privacy tradeoffs in databases an information theoretic approach"

Connected through hyperedge HE-2 (induced by source paper "principal inertia components and applications"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "the most informative boolean function conjecture holds for high noise"
    - "bounds between contraction coefficients"
    - "on the duality of additivity and tensorization"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "remarks on the most informative function conjecture at fixed mean"
    - "notes on information theoretic privacy"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-3 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime"):
  with members:
    - "privacy aware mmse estimation"
    - "hypothesis testing in the high privacy limit"
    - "f divergence inequalities"
    - "on maximal correlation mutual information and data privacy"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "bounds on inference"
    - "the awgn red alert problem"

Connected through hyperedge HE-4 (induced by source paper "two moment inequalities for r enyi entropy and mutual information"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "f divergence inequalities"
    - "conditional central limit theorems for gaussian projections"

Connected through hyperedge HE-5 (induced by source paper "hypothesis testing under maximal leakage privacy constraints"):
  with members:
    - "hypothesis testing in the high privacy limit"

Connected through hyperedge HE-6 (induced by source paper "hypothesis testing in the high privacy limit"):
  with members:
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-7 (induced by source paper "communication theoretic data analytics"):
  with members:
    - "role discovery in networks"
    - "linear information coupling problems"

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)