human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "information nonanticipative rate distortion function and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transfer entropy in mdps with temporal logic specifications")
  Member "nonanticipative rate distortion function and relations to filtering theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian sources")
  Member "variational equalities of directed information and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information nonanticipative rate distortion function and its applications")
  Member "directed information on abstract spaces properties and extremum problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback")
  Member "nonanticipative rate distortion function and filtering theory a weak convergence approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational equalities of directed information and applications")
  Member "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources")

Question: Please tell me which class the source paper 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)