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: "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"
  Abstract: We deal with zero-delay source coding of a vector Gaussian autoregressive (AR) source subject to an average mean squared error (MSE) fidelity criterion. Toward this end, we consider the nonanticipative rate distortion function (NRDF) which is a lower bound to the causal and zero-delay rate distortion function (RDF). We use the realization scheme with feedback proposed in [1] to model the corresponding optimal "test-channel" of the NRDF, when considering vector Gaussian AR(1) sources subject to an average MSE distortion. We give conditions on the vector Gaussian AR(1) source to ensure asymptotic stationarity of the realization scheme (bounded performance). Then, we encode the vector innovations due to Kalman filtering via lattice quantization with subtractive dither and memoryless entropy coding. This coding scheme provides a tight upper bound to the zero-delay Gaussian RDF. We extend this result to vector Gaussian AR sources of any finite order. Further, we show that for infinite dimensional vector Gaussian AR sources of any finite order, the NRDF coincides with the zero-delay RDF. Our theoretical framework is corroborated with a simulation example.

Connected through hyperedge HE-1 (induced by source paper "zero delay rate distortion via filtering for vector valued gaussian sources"):
  with members:
    - "filtering with fidelity for time varying gauss markov processes"
    - "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 "stationarity in the realizations of the causal rate distortion function for one sided stationary sources"):
  with members:
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "information nonanticipative rate distortion function and its applications"
    - "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 "rate cost tradeoffs in control"):
  with members:
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "data compression with low distortion and finite blocklength"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "a coding theorem for a class of stationary channels with feedback"
    - "the capacity of channels with 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)