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 lower bound on the bayesian mse based on the optimal bias function"
  Abstract: A lower bound on the minimum mean-squared error (MSE) in a Bayesian estimation problem is proposed in this paper. This bound utilizes a well-known connection to the deterministic estimation setting. Using the prior distribution, the bias function which minimizes the Cramer-Rao bound can be determined, resulting in a lower bound on the Bayesian MSE. The bound is developed for the general case of a vector parameter with an arbitrary probability distribution, and is shown to be asymptotically tight in both the high and low signal-to-noise ratio regimes. A numerical study demonstrates several cases in which the proposed technique is both simpler to compute and tighter than alternative methods.

Connected through hyperedge HE-1 (induced by source paper "performance of time delay estimation in a cognitive radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation in dispersed spectrum cognitive radio systems"

Connected through hyperedge HE-2 (induced by source paper "lower bounds on parameter modulation estimation under bandwidth constraints"):
  with members:
    - "exponential error bounds on parameter modulation estimation for discrete memoryless channels"
    - "on optimum parameter modulation estimation from a large deviations perspective"
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"
    - "unequal error protection an information theoretic perspective"
    - "error exponents for broadcast channels with degraded message sets"

Connected through hyperedge HE-3 (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"
    - "data processing inequalities based on a certain structured class of information measures with application to estimation theory"

Connected through hyperedge HE-4 (induced by source paper "on optimum parameter modulation estimation from a large deviations perspective"):
  with members:
    - "data processing inequalities based on a certain structured class of information measures with application to estimation theory"
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"

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)