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: "nonanticipative rate distortion function for general source channel matching"
  Abstract: In this paper we invoke a nonanticipative information Rate Distortion Function (RDF) for sources with memory, and we analyze its importance in probabilistic matching of the source to the channel so that transmission of a symbol-by-symbol code with memory without anticipation is optimal, with respect to an average distortion and excess distortion probability. We show achievability of the symbol-by-symbol code with memory without anticipation, and we evaluate the probabilistic performance of the code for a Markov source.

Connected through hyperedge HE-1 (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"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"
    - "on directed information and gambling"
    - "feedback capacity of the first order moving average gaussian channel"

Connected through hyperedge HE-2 (induced by source paper "variational equalities of directed information and applications"):
  with members:
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "directed information on abstract spaces properties and variational equalities"
    - "directed information on abstract spaces properties and extremum problems"
    - "nonanticipative rate distortion function and filtering theory a weak convergence approach"
    - "computable bounds for rate distortion with feed forward for stationary and ergodic sources"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "extension of the blahut arimoto algorithm for maximizing directed information"
    - "on delayed sequential coding of correlated sources"

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)