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: "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.

Connected through hyperedge HE-1 (induced by source paper "transfer entropy in mdps with temporal logic specifications"):
  with members:
    - "a learning based optimal human robot collaboration with linear temporal logic constraints"
    - "optimal control in markov decision processes via distributed optimization"
    - "verification of markov decision processes using learning algorithms"
    - "optimal control of mdps with temporal logic constraints"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "stationarity in the realizations of the causal rate distortion function for one sided stationary sources"):
  with members:
    - "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "capacity of binary state symmetric channel with and without feedback and transmission cost"):
  with members:
    - "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"
    - "on directed information and gambling"
    - "feedback capacity of the first order moving average gaussian channel"

Connected through hyperedge HE-4 (induced by source paper "applications of information nonanticipative rate distortion function"):
  with members:
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "variational equalities of directed information and applications"
    - "directed information on abstract spaces properties and extremum problems"
    - "nonanticipative rate distortion function and filtering theory a weak convergence approach"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

Connected through hyperedge HE-5 (induced by source paper "directed information on abstract spaces properties and variational equalities"):
  with members:
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "optimal nonanticipative estimation schemes for time varying gauss markov processes"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "capacity of a post channel with and without feedback"
    - "optimal nonstationary reproduction distribution for nonanticipative rdf on abstract alphabets"

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)