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 "information nonanticipative rate distortion function and its applications"):
  Abstract: This paper investigates applications of nonanticipative Rate Distortion Function (RDF) in a) zero-delay Joint Source-Channel Coding (JSCC) design based on average and excess distortion probability, b) in bounding the Optimal Performance Theoretically Attainable (OPTA) by noncausal and causal codes, and computing the Rate Loss (RL) of zero-delay and causal codes with respect to noncausal codes. These applications are described using two running examples, the Binary Symmetric Markov Source with parameter p, (BSMS(p)) and the multidimensional partially observed Gaussian-Markov source. For the multidimensional Gaussian-Markov source with square error distortion, the solution of the nonanticipative RDF is derived, its operational meaning using JSCC design via a noisy coding theorem is shown by providing the optimal encoding-decoding scheme over a vector Gaussian channel, and the RL of causal and zero-delay codes with respect to noncausal codes is computed. For the BSMS(p) with Hamming distortion, the solution of the nonanticipative RDF is derived, the RL of causal codes with respect to noncausal codes is computed, and an uncoded noisy coding theorem based on excess distortion probability is shown. The information nonanticipative RDF is shown to be equivalent to the nonanticipatory -entropy, which corresponds to the classical RDF with an additional causality or nonanticipative condition imposed on the optimal reproduction conditional distribution. Index Terms Nonanticipative RDF, sources with memory, Joint Source-Channel Coding (JSCC), Binary Symmetric Markov Source (BSMS), multidimensional stationary Gaussian-Markov source, excess distortion probability, bounds.

Includes members:
  Member "nonanticipative rate distortion function and relations to filtering theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian sources")
  Member "on zero delay source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero delay source channel coding with a one bit adc front end and correlated side information at the receiver")
  Member "applications of information nonanticipative rate distortion function":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity of binary state symmetric channel with and without feedback and transmission cost")
  Member "directed information on abstract spaces properties and variational equalities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources")
  Member "variational equalities of directed information and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applications of information nonanticipative rate distortion function")
  Member "on the relation of nonanticipative rate distortion function and filtering theory":
    also connected through other hyperedges:
      (none)
  Member "directed information on abstract spaces properties and extremum problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback")
  Member "lossy joint source channel coding in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finite sample analysis of image registration")

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)