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: "universal estimation of directed information"
  Abstract: Four estimators of the directed information rate between a pair of jointly stationary ergodic finite-alphabet processes are proposed, based on universal probability assignments. The first one is a Shannon-McMillan-Breiman-type estimator, similar to those used by Verdu in 2005 and Cai in 2006 for estimation of other information measures. We show the almost sure and L1 convergence properties of the estimator for any underlying universal probability assignment. The other three estimators map universal probability assignments to different functionals, each exhibiting relative merits such as smoothness, nonnegativity, and boundedness. We establish the consistency of these estimators in almost sure and L1 senses, and derive near-optimal rates of convergence in the minimax sense under mild conditions. These estimators carry over directly to estimating other information measures of stationary ergodic finite-alphabet processes, such as entropy rate and mutual information rate, with near-optimal performance and provide alternatives to classical approaches in the existing literature. Guided by these theoretical results, the proposed estimators are implemented using the context-tree weighting algorithm as the universal probability assignment. Experiments on synthetic and real data are presented, demonstrating the potential of the proposed schemes in practice and the utility of directed information estimation in detecting and measuring causal influence and delay.

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"
    - "estimating the directed information and testing for causality"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-2 (induced by source paper "fundamental limits of online learning an entropic innovations viewpoint"):
  with members:
    - "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective"
    - "generic bounds on the maximum deviations in sequential prediction an information theoretic analysis"
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-4 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "ccmi classifier based conditional mutual information estimation"
    - "a brief introduction to machine learning for engineers"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "information flow in computational systems"):
  with members:
    - "estimating mutual information for discrete continuous mixtures"
    - "bivariate measure of redundant information"
    - "directed information graphs"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-6 (induced by source paper "on the bias of directed information estimators"):
  with members:
    - "k nn estimation of directed information"
    - "estimating the directed information and testing for causality"
    - "strong completeness and faithfulness in bayesian networks"
    - "directed information graphs"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "active distribution learning from indirect samples"):
  with members:
    - "adaptive estimation of shannon entropy"
    - "the complexity of estimating r enyi entropy"
    - "active sequential hypothesis testing"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "near optimal bayesian active learning with noisy observations"

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)