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: "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective"
  Abstract: In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the "innovation" is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szeg\"o formula and Wiener-Masani formula known from linear prediction theory.

Connected through hyperedge HE-1 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises"):
  with members:
    - "youla coding and computation of gaussian feedback capacity"
    - "feedback capacity of stationary gaussian channels further examined"
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "feedback capacity of gaussian channels revisited"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity achieving feedback schemes for gaussian finite state markov channels with channel state information"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the feedback capacity of power constrained gaussian noise channels with memory"

Connected through hyperedge HE-2 (induced by source paper "fundamental limits of online learning an entropic innovations viewpoint"):
  with members:
    - "generic bounds on the maximum deviations in sequential prediction an information theoretic analysis"
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"
    - "universal estimation of directed information"

Connected through hyperedge HE-3 (induced by source paper "fundamental limitations in sequential prediction and recursive algorithms l p bounds via an entropic analysis"):
  with members:
    - "generic bounds on the maximum deviations in sequential prediction an information theoretic analysis"
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "generic bounds on the maximum deviations in sequential prediction an information theoretic analysis"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"

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)