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 and gambling"
  Abstract: We study the problem of gambling in horse races with causal side information and show that Massey's directed information characterizes the increment in the maximum achievable capital growth rate due to the availability of side information. This result gives a natural interpretation of directed information $I(Y^n \to X^n)$ as the amount of information that $Y^n$ \emph{causally} provides about $X^n$. Extensions to stock market portfolio strategies and data compression with causal side information are also discussed.

Connected through hyperedge HE-1 (induced by source paper "maximum causal entropy specification inference from demonstrations"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning task specifications from demonstrations"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "spudd stochastic planning using decision diagrams"

Connected through hyperedge HE-2 (induced by source paper "generalized maximum causal entropy for inverse reinforcement learning"):
  with members:
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "dialogue generation from imitation learning to inverse reinforcement learning"):
  with members:
    - "towards diverse text generation with inverse reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "adversarial learning for neural dialogue generation"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "generative adversarial imitation learning"
    - "topic aware neural response generation"

Connected through hyperedge HE-4 (induced by source paper "capacity of binary state symmetric channel with and without feedback and transmission cost"):
  with members:
    - "information nonanticipative rate distortion function and its applications"
    - "applications of information nonanticipative rate distortion function"
    - "capacity of a post channel with and without feedback"
    - "nonanticipative rate distortion function for general source channel matching"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"
    - "feedback capacity of the first order moving average gaussian channel"

Connected through hyperedge HE-5 (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-6 (induced by source paper "directed information causal estimation and communication in continuous time"):
  with members:
    - "mutual information relative entropy and estimation in the poisson channel"
    - "lecture notes on network information theory"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "the poisson channel with side information"
    - "feedback capacity of the compound channel"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "causal dependence tree approximations of joint distributions for multiple random processes"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "a generalization of the chow liu algorithm and its application to statistical learning"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "finite state channels with time invariant deterministic feedback"
    - "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)