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: "a note on rate distortion functions for nonstationary gaussian autoregressive processes"
  Abstract: Source coding theorems and Shannon rate-distortion functions were studied for the discrete-time Wiener process by Berger and generalized to nonstationary Gaussian autoregressive processes by Gray and by Hashimoto and Arimoto. Hashimoto and Arimoto provided an example apparently contradicting the methods used in Gray, implied that Gray's rate-distortion evaluation was not correct in the nonstationary case, and derived a new formula that agreed with previous results for the stationary case and held in the nonstationary case. In this correspondence it is shown that the rate-distortion formulas of Gray and Hashimoto and Arimoto are in fact consistent and that the example of of Hashimoto and Arimoto does not form a counter example to the methods or results of the earlier paper. Their results do provide an alternative, but equivalent, formula for the rate-distortion function in the nonstationary case and they provide a concrete example that the classic Kolmogorov formula differs from the autoregressive formula when the autoregressive source is not stationary. Some observations are offered on the different versions of the Toeplitz asymptotic eigenvalue distribution theorem used in the two papers to emphasize how a slight modification of the classic theorem avoids the problems with certain singularities.

Connected through hyperedge HE-1 (induced by source paper "from parameter estimation to dispersion of nonstationary gauss markov processes"):
  with members:
    - "the dispersion of the gauss markov source"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "finite time identification in unstable linear systems"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-2 (induced by source paper "the dispersion of the gauss markov source"):
  with members:
    - "second order region for gray wyner network"
    - "on the dispersions of three network information theory problems"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "the dispersion of joint source channel coding"

Connected through hyperedge HE-3 (induced by source paper "the entropy gain of linear time invariant filters and some of its implications"):
  with members:
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "characterization of information channels for asymptotic mean stationarity and stochastic stability of non stationary unstable linear systems"):
  with members:
    - "linear error correcting codes with anytime reliability"
    - "implicit and explicit communication in decentralized control"
    - "unequal error protection an information theoretic perspective"
    - "error exponents for variable length block codes with feedback and cost constraints"
    - "finite state channels with time invariant deterministic 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)