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: "multiterminal source coding with an entropy based distortion measure"
  Abstract: In this paper, we consider a class of multiterminal source coding problems, each subject to distortion constraints computed using a specific, entropy-based, distortion measure. We provide the achievable rate distortion region for two cases and, in so doing, we demonstrate a relationship between the lossy multiterminal source coding problems with our specific distortion measure and (1) the canonical Slepian-Wolf lossless distributed source coding network, and (2) the Ahlswede-K\"{o}rner-Wyner source coding with side information problem in which only one of the sources is recovered losslessly.

Connected through hyperedge HE-1 (induced by source paper "vector gaussian ceo problem under logarithmic loss"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss and applications"
    - "universality of logarithmic loss in lossy compression"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "justification of logarithmic loss via the benefit of side information"
    - "rate region of the vector gaussian ceo problem with the trace distortion constraint"
    - "secure source coding with a public helper"
    - "an outer bound for the vector gaussian ceo problem"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-2 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"

Connected through hyperedge HE-3 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "on the universality of the logistic loss function"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-4 (induced by source paper "a tunable measure for information leakage"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"

Connected through hyperedge HE-5 (induced by source paper "nonlinear information bottleneck"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "uncertainty in the variational information bottleneck"
    - "fixing a broken elbo"
    - "opening the black box of deep neural networks via information"
    - "estimating mixture entropy with pairwise distances"
    - "deep variational information bottleneck"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-6 (induced by source paper "universality of logarithmic loss in lossy compression"):
  with members:
    - "justification of logarithmic loss via the benefit of side information"
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "rateless lossy compression via the extremes"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-7 (induced by source paper "privacy guaranteed two agent interactions using information theoretic mechanisms"):
  with members:
    - "extremal mechanisms for local differential privacy"
    - "from the information bottleneck to the privacy funnel"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"
    - "competitive privacy in the smart grid an information theoretic approach"
    - "the infinite message limit of two terminal interactive source coding"

Connected through hyperedge HE-8 (induced by source paper "secrecy in cascade networks"):
  with members:
    - "secure cascade channel synthesis"
    - "optimal equivocation in secrecy systems a special case of distortion based characterization"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "secrecy is cheap if the adversary must reconstruct"
    - "multiterminal source coding under logarithmic loss"

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)