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: "data processing inequalities based on a certain structured class of information measures with application to estimation theory"
  Abstract: We study data processing inequalities that are derived from a certain class of generalized information measures, where a series of convex functions and multiplicative likelihood ratios are nested alternately. While these information measures can be viewed as a special case of the most general Zakai-Ziv generalized information measure, this special nested structure calls for attention and motivates our study. Specifically, a certain choice of the convex functions leads to an information measure that extends the notion of the Bhattacharyya distance (or the Chernoff divergence): While the ordinary Bhattacharyya distance is based on the (weighted) geometric mean of two replicas of the channel's conditional distribution, the more general information measure allows an arbitrary number of such replicas. We apply the data processing inequality induced by this information measure to a detailed study of lower bounds of parameter estimation under additive white Gaussian noise (AWGN) and show that in certain cases, tighter bounds can be obtained by using more than two replicas. While the resulting lower bound may not compete favorably with the best bounds available for the ordinary AWGN channel, the advantage of the new lower bound, relative to the other bounds, becomes significant in the presence of channel uncertainty, like unknown fading. This different behavior in the presence of channel uncertainty is explained by the convexity property of the information measure.

Connected through hyperedge HE-1 (induced by source paper "exponential error bounds on parameter modulation estimation for discrete memoryless channels"):
  with members:
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "on optimum parameter modulation estimation from a large deviations perspective"
    - "a lower bound on the bayesian mse based on the optimal bias function"

Connected through hyperedge HE-2 (induced by source paper "fundamental inequalities and identities involving mutual and directed informations in closed loop systems"):
  with members:
    - "the information flow and capacity of channels with noisy feedback"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "a coding theorem for a class of stationary channels with feedback"
    - "achieving the gaussian rate distortion function by prediction"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-3 (induced by source paper "on optimum parameter modulation estimation from a large deviations perspective"):
  with members:
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"
    - "a lower bound on the bayesian mse based on the optimal bias function"

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)