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: "notes on kullback leibler divergence and likelihood"
  Abstract: The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two probability distributions. Although difficult to understand by examining the equation, an intuition and understanding of the KL divergence arises from its intimate relationship with likelihood theory. We discuss how KL divergence arises from likelihood theory in an attempt to provide some intuition and reserve a rigorous (but rather simple) derivation for the appendix. Finally, we comment on recent applications of KL divergence in the neural coding literature and highlight its natural application.

Connected through hyperedge HE-1 (induced by source paper "wasserstein distance guided cross domain learning"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "coupled generative adversarial networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "automatic moth detection from trap images for pest management"

Connected through hyperedge HE-2 (induced by source paper "pull out all the stops textual analysis via punctuation sequences"):
  with members:
    - "robustness of sentence length measures in written texts"
    - "a standardized project gutenberg corpus for statistical analysis of natural language and quantitative linguistics"

Connected through hyperedge HE-3 (induced by source paper "scalable and accurate online feature selection for big data"):
  with members:
    - "discovering support and affiliated features from very high dimensions"
    - "towards ultrahigh dimensional feature selection for big data"
    - "manifold elastic net a unified framework for sparse dimension reduction"

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)