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: "chernoff information of bottleneck gaussian trees"
  Abstract: In this paper, our objective is to find out the determining factors of Chernoff information in distinguishing a set of Gaussian trees. In this set, each tree can be attained via an edge removal and grafting operation from another tree. This is equivalent to asking for the Chernoff information between the most-likely confused, i.e. "bottleneck", Gaussian trees, as shown to be the case in ML estimated Gaussian tree graphs lately. We prove that the Chernoff information between two Gaussian trees related through an edge removal and a grafting operation is the same as that between two three-node Gaussian trees, whose topologies and edge weights are subject to the underlying graph operation. In addition, such Chernoff information is shown to be determined only by the maximum generalized eigenvalue of the two Gaussian covariance matrices. The Chernoff information of scalar Gaussian variables as a result of linear transformation (LT) of the original Gaussian vectors is also uniquely determined by the same maximum generalized eigenvalue. What is even more interesting is that after incorporating the cost of measurements into a normalized Chernoff information, Gaussian variables from LT have larger normalized Chernoff information than the one based on the original Gaussian vectors, as shown in our proved bounds

Connected through hyperedge HE-1 (induced by source paper "successive synthesis of latent gaussian trees"):
  with members:
    - "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"
    - "gaussian secure source coding and wyner s common information"
    - "a survey on latent tree models and applications"
    - "distributed channel synthesis"

Connected through hyperedge HE-2 (induced by source paper "chernoff information between gaussian trees"):
  with members:
    - "tight bounds for symmetric divergence measures and a new inequality relating f divergences"
    - "on model misspecification and kl separation for gaussian graphical models"
    - "representation learning a review and new perspectives"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "topological and algebraic properties of chernoff information between gaussian graphs"):
  with members:
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (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"
    - "gaussian secure source coding and wyner s common information"
    - "a survey on latent tree models and applications"

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)