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: "zero delay rate distortion via filtering for vector valued gaussian sources"
  Abstract: We deal with zero-delay source coding of a vector-valued Gauss–Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF), which is a lower bound to the causal optimal performance theoretically attainable function (or simply causal RDF) and operational zero-delay RDF. We recall the realization that corresponds to the optimal “test-channel” of the Gaussian NRDF, when considering a vector Gauss–Markov source subject to a MSE distortion in the finite time horizon. Then, we introduce sufficient conditions to show existence of solution for this problem in the infinite time horizon (or asymptotic regime). For the asymptotic regime, we use the asymptotic characterization of the Gaussian NRDF to provide a new equivalent realization scheme with feedback, which is characterized by a resource allocation (reverse-waterfilling) problem across the dimension of the vector source. We leverage the new realization to derive a predictive coding scheme via lattice quantization with subtractive dither and joint memoryless entropy coding. This coding scheme offers an upper bound to the operational zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then for    $r$   active dimensions of the vector Gauss–Markov source the gap between the obtained lower and theoretical upper bounds is less than or equal to    $0.254r + 1$   bits/vector. However, we further show that it is possible when we use vector quantization, and assume infinite dimensional Gauss–Markov sources to make the previous gap to be negligible, i.e., Gaussian NRDF approximates the operational zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian sources of any finite memory under mild conditions. Our theoretical framework is demonstrated with illustrative numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources"):
  with members:
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "directed information on abstract spaces properties and variational equalities"
    - "internet of things iot a vision architectural elements and future directions"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

Connected through hyperedge HE-2 (induced by source paper "on zero delay rdf for vector valued gauss markov sources with additional noise"):
  with members:
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "indirect rate distortion function of a binary i i d source"
    - "directed information on abstract spaces properties and variational equalities"
    - "internet of things iot a vision architectural elements and future directions"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

Connected through hyperedge HE-3 (induced by source paper "the effect of time delay on the average data rate and performance in networked control systems"):
  with members:
    - "interplay between transmission delay average data rate and performance in output feedback control over digital communication channels"
    - "rate cost tradeoffs in control"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "stability analysis of continuous time switched systems with a random switching signal"

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)