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: "on directed information theory and granger causality graphs"
  Abstract: Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

Connected through hyperedge HE-1 (induced by source paper "a sample path measure of causal influence"):
  with members:
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "universal estimation of directed information"
    - "directed information graphs"

Connected through hyperedge HE-2 (induced by source paper "bounded degree approximations of stochastic networks"):
  with members:
    - "the relation between granger causality and directed information theory a review"
    - "on learning discrete graphical models using greedy methods"

Connected through hyperedge HE-3 (induced by source paper "measuring information transfer in a soft robotic arm"):
  with members:
    - "information driven self organization of complex robotic behaviors"

Connected through hyperedge HE-4 (induced by source paper "efficient transfer entropy analysis of non stationary neural time series"):
  with members:
    - "assessing coupling dynamics from an ensemble of time series"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "bivariate measure of redundant information"
    - "towards a synergy based approach to measuring information modification"
    - "shared information new insights and problems in decomposing information in complex systems"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-5 (induced by source paper "directed information on abstract spaces properties and variational equalities"):
  with members:
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "optimal nonanticipative estimation schemes for time varying gauss markov processes"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information nonanticipative rate distortion function and its applications"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "capacity of a post channel with and without feedback"

Connected through hyperedge HE-6 (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"
    - "the relation between granger causality and directed information theory a review"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "the relation between granger causality and directed information theory a review"):
  with members:
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "relating granger causality to directed information theory for networks of stochastic processes"
    - "a coding theorem for a class of stationary channels with feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-8 (induced by source paper "directed information and pearl s causal calculus"):
  with members:
    - "information theoretic viewpoints on optimal causal coding decoding problems"
    - "causal dependence tree approximations of joint distributions for multiple random processes"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "the capacity of channels with feedback"

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)