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: "k nn estimation of directed information"
  Abstract: This report studies data-driven estimation of the directed information (DI) measure between two{em discrete-time and continuous-amplitude} random process, based on the $k$-nearest-neighbors ($k$-NN) estimation framework. Detailed derivations of two $k$-NN estimators are provided. The two estimators differ in the metric based on which the nearest-neighbors are found. To facilitate the estimation of the DI measure, it is assumed that the observed sequences are (jointly) Markovian of order $m$. As $m$ is generally not known, a data-driven method (that is also based on the $k$-NN principle) for estimating $m$ from the observed sequences is presented. An exhaustive numerical study shows that the discussed $k$-NN estimators perform well even for relatively small number of samples (few thousands). Moreover, it is shown that the discussed estimators are capable of accurately detecting linear as well as non-linear causal interactions.

Connected through hyperedge HE-1 (induced by source paper "adversarial orthogonal regression two non linear regressions for causal inference"):
  with members:
    - "mine mutual information neural estimation"
    - "adam a method for stochastic optimization"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "directed information graphs"
    - "information theoretic measures of influence based on content dynamics"
    - "information transfer in social media"

Connected through hyperedge HE-2 (induced by source paper "on the bias of directed information estimators"):
  with members:
    - "estimating the directed information and testing for causality"
    - "strong completeness and faithfulness in bayesian networks"
    - "universal estimation of directed information"
    - "directed information graphs"

Connected through hyperedge HE-3 (induced by source paper "measuring sample path causal influences with relative entropy"):
  with members:
    - "on the bias of directed information estimators"
    - "a sample path measure of causal influence"
    - "consistent tomography under partial observations over adaptive networks"
    - "estimating the directed information and testing for causality"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "strong completeness and faithfulness in bayesian networks"
    - "causal networks semantics and expressiveness"
    - "universal estimation of directed information"

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)