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: "the relation between granger causality and directed information theory a review"
  Abstract: This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory. The definitions of Granger causality based on prediction are recalled, and the importance of the observation set is discussed. We present the definitions based on conditional independence. The notion of instantaneous coupling is included in the definitions. The concept of Granger causality graphs is discussed. We present directed information theory from the perspective of studies of causal influences between stochastic processes. Causal conditioning appears to be the cornerstone for the relation between information theory and Granger causality. In the bivariate case, the fundamental measure is the directed information, which decomposes as the sum of the transfer entropies and a term quantifying instantaneous coupling. We show the decomposition of the mutual information into the sums of the transfer entropies and the instantaneous coupling measure, a relation known for the linear Gaussian case. We study the multivariate case, showing that the useful decomposition is blurred by instantaneous coupling. The links are further developed by studying how measures based on directed information theory naturally emerge from Granger causality inference frameworks as hypothesis testing.

Connected through hyperedge HE-1 (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-2 (induced by source paper "optimal causal imputation for control"):
  with members:
    - "causal decision trees"
    - "residential demand response targeting using machine learning with observational data"

Connected through hyperedge HE-3 (induced by source paper "strong converses for group testing in the finite blocklength regime"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "bitwise map algorithm for group testing based on holographic transformation"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-4 (induced by source paper "estimating the directed information and testing for causality"):
  with members:
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "information networks with in block memory"
    - "universal estimation of directed information"
    - "detection and isolation of link failures under the agreement protocol"
    - "directed information causal estimation and communication in continuous time"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "feedback capacity of the compound channel"
    - "the capacity region of the degraded finite state broadcast channel"

Connected through hyperedge HE-5 (induced by source paper "justification of logarithmic loss via the benefit of side information"):
  with members:
    - "information measures the curious case of the binary alphabet"
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "the complexity of estimating r enyi entropy"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "universal estimation of directed information"
    - "improved information gain estimates for decision tree induction"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-6 (induced by source paper "bounded degree approximations of stochastic networks"):
  with members:
    - "on learning discrete graphical models using greedy methods"
    - "on directed information theory and granger causality graphs"

Connected through hyperedge HE-7 (induced by source paper "directed information graphs"):
  with members:
    - "justification of logarithmic loss via the benefit of side information"
    - "causal networks semantics and expressiveness"
    - "universal estimation of directed information"
    - "causal conditioning and instantaneous coupling in causality graphs"
    - "information theoretic measures of influence based on content dynamics"
    - "computable bounds for rate distortion with feed forward for stationary and ergodic sources"
    - "directed information and pearl s causal calculus"
    - "causal dependence tree approximations of joint distributions for multiple random processes"

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)