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: "estimating the directed information and testing for causality"
  Abstract: The problem of estimating the directed information rate between two discrete processes $\{X_n\}$ and $\{Y_n\}$ via the plug-in (or maximum-likelihood) estimator is considered. When the joint process $\{(X_n,Y_n)\}$ is a Markov chain of a given memory length, the plug-in estimator is shown to be asymptotically Gaussian and to converge at the optimal rate $O(1/\sqrt{n})$ under appropriate conditions; this is the first estimator that has been shown to achieve this rate. An important connection is drawn between the problem of estimating the directed information rate and that of performing a hypothesis test for the presence of causal influence between the two processes. Under fairly general conditions, the null hypothesis, which corresponds to the absence of causal influence, is equivalent to the requirement that the directed information rate be equal to zero. In that case a finer result is established, showing that the plug-in converges at the faster rate $O(1/n)$ and that it is asymptotically $\chi^2$-distributed. This is proved by showing that this estimator is equal to (a scalar multiple of) the classical likelihood ratio statistic for the above hypothesis test. Finally it is noted that these results facilitate the design of an actual likelihood ratio test for the presence or absence of causal influence.

Connected through hyperedge HE-1 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator"):
  with members:
    - "computing the feedback capacity of finite state channels using reinforcement learning"
    - "conditional mutual information neural estimator"
    - "deep learning for channel coding via neural mutual information estimation"
    - "neural entropic estimation a faster path to mutual information estimation"
    - "itene intrinsic transfer entropy neural estimator"
    - "mine mutual information neural estimation"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

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

Connected through hyperedge HE-3 (induced by source paper "estimating transfer entropy via copula entropy"):
  with members:
    - "discovering association with copula entropy"
    - "mutual information is copula entropy"

Connected through hyperedge HE-4 (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"
    - "k nn estimation of directed information"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "k nn estimation of directed information"):
  with members:
    - "minimax estimation of functionals of discrete distributions"
    - "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)