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: "applications of information nonanticipative rate distortion function"
  Abstract: The objective of this paper is to further investigate various applications of information Nonanticipative Rate Distortion Function (NRDF) by discussing two working examples, the Binary Symmetric Markov Source with parameter $p$ (BSMS($p$)) with Hamming distance distortion, and the multidimensional partially observed Gaussian-Markov source. For the BSMS($p$), we give the solution to the NRDF, and we use it to compute the Rate Loss (RL) of causal codes with respect to noncausal codes. For the multidimensional Gaussian-Markov source, we give the solution to the NRDF, we show its operational meaning via joint source-channel matching over a vector of parallel Gaussian channels, and we compute the RL of causal and zero-delay codes with respect to noncausal codes.

Connected through hyperedge HE-1 (induced by source paper "information nonanticipative rate distortion function and its applications"):
  with members:
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "on zero delay source channel coding"
    - "directed information on abstract spaces properties and variational equalities"
    - "variational equalities of directed information and applications"
    - "on the relation of nonanticipative rate distortion function and filtering theory"
    - "directed information on abstract spaces properties and extremum problems"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-2 (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"
    - "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"
    - "on directed information and gambling"
    - "feedback capacity of the first order moving average gaussian channel"

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)