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: "gaussian secure source coding and wyner s common information"
  Abstract: We study secure source-coding with causal disclosure, under the Gaussian distribution. The optimality of Gaussian auxiliary random variables is shown in various scenarios. We explicitly characterize the tradeoff between the rates of communication and secret key. This tradeoff is the result of a mutual information optimization under Markov constraints. As a corollary, we deduce a general formula for Wyner's Common Information in the Gaussian setting.

Connected through hyperedge HE-1 (induced by source paper "common information components analysis"):
  with members:
    - "relaxed wyner s common information"

Connected through hyperedge HE-2 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (induced by source paper "algebraic properties of wyner common information solution under graphical constraints"):
  with members:
    - "latent factor analysis of gaussian distributions under graphical constraints"
    - "communication requirements for generating correlated random variables"

Connected through hyperedge HE-4 (induced by source paper "successive synthesis of latent gaussian trees"):
  with members:
    - "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"
    - "chernoff information of bottleneck gaussian trees"
    - "a survey on latent tree models and applications"
    - "distributed channel synthesis"

Connected through hyperedge HE-5 (induced by source paper "extracting wyner s common information using polar codes and polar lattices"):
  with members:
    - "distributed simulation of continuous random variables"
    - "polar lattices for lossy compression"
    - "construction of capacity achieving lattice codes polar lattices"
    - "the lossy common information of correlated sources"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "achieving the capacity of any dmc using only polar codes"
    - "how to construct polar codes"

Connected through hyperedge HE-6 (induced by source paper "distributed simulation of continuous random variables"):
  with members:
    - "exact common information"
    - "distributed channel synthesis"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"
    - "the common information of n dependent random variables"

Connected through hyperedge HE-7 (induced by source paper "layered synthesis of latent gaussian trees"):
  with members:
    - "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"
    - "distributed simulation of continuous random variables"
    - "chernoff information of bottleneck gaussian trees"
    - "a survey on latent tree models and applications"

Connected through hyperedge HE-8 (induced by source paper "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"):
  with members:
    - "classifying unrooted gaussian trees under privacy constraints"
    - "distributed channel synthesis"

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)