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: "variational equalities of directed information and applications"
  Abstract: In this paper we introduce two variational equalities of directed information, which are analogous to those of mutual information employed in the Blahut-Arimoto Algorithm (BAA). Subsequently, we introduce nonanticipative Rate Distortion Function (RDF) ${R}^{na}_{0,n}(D)$ defined via directed information introduced in [1], and we establish its equivalence to Gorbunov-Pinsker's nonanticipatory $\epsilon$-entropy $R^{\varepsilon}_{0,n}(D)$. By invoking certain results we first establish existence of the infimizing reproduction distribution for ${R}^{na}_{0,n}(D)$, and then we give its implicit form for the stationary case. Finally, we utilize one of the variational equalities and the closed form expression of the optimal reproduction distribution to provide an algorithm for the computation of ${R}^{na}_{0,n}(D)$.

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"
    - "applications of information nonanticipative rate distortion function"
    - "directed information on abstract spaces properties and variational equalities"
    - "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 "applications of information nonanticipative rate distortion function"):
  with members:
    - "information nonanticipative rate distortion function and its applications"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "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-3 (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 nonanticipative rate distortion function and its applications"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "source channel matching for sources with memory"):
  with members:
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "capacity of a post channel with and without feedback"

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)