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: "filtering with fidelity for time varying gauss markov processes"
  Abstract: In this paper, we revisit the relation between Nonanticipative Rate Distortion (NRD) theory and real-time realizable filtering theory. Specifically, we derive a closed form expression for the optimal nonstationary (time-varying) reproduction distribution of the Finite Time Horizon (FTH) Nonanticipative Rate Distortion Function (NRDF) and we establish the connection to real-time realizable filtering theory via a realization scheme utilizing fully observable multidimensional time-varying Gauss-Markov processes. We also derive a universal lower bound to the mean square error for time-varying multidimensional Gauss-Markov sources. Then, as an application we consider time-varying multidimensional Gauss-Markov processes, and we provide the optimal filter with respect to a mean square error constraint. Unlike classical filtering theory, our filtering approach based on FTHI NRDF is performed with waterfilling. Our theoretical results are demonstrated via illustrative examples.

Connected through hyperedge HE-1 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian 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"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "directed information on abstract spaces properties and variational equalities"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "achieving the gaussian rate distortion function by prediction"

Connected through hyperedge HE-2 (induced by source paper "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"):
  with members:
    - "directed information on abstract spaces properties and variational equalities"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

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)