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: "threshold effects in parameter estimation as phase transitions in statistical mechanics"
  Abstract: Threshold effects in the estimation of parameters of non-linearly modulated, continuous-time, wide-band waveforms, are examined from a statistical physics perspective. These threshold effects are shown to be analogous to phase transitions of certain disordered physical systems in thermal equilibrium. The main message, in this work, is in demonstrating that this physical point of view may be insightful for understanding the interactions between two or more parameters to be estimated, from the aspects of the threshold effect.

Connected through hyperedge HE-1 (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"
    - "unequal error protection an information theoretic perspective"
    - "error exponents for broadcast channels with degraded message sets"
    - "a lower bound on the bayesian mse based on the optimal bias function"

Connected through hyperedge HE-2 (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"
    - "a lower bound on the bayesian mse based on the optimal bias function"

Connected through hyperedge HE-3 (induced by source paper "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"):
  with members:
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "rate distortion function via minimum mean square error estimation"
    - "how could the replica method improve accuracy of performance assessment of channel coding"
    - "life above threshold from list decoding to area theorem and mse"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "data processing inequalities based on a certain structured class of information measures with application to estimation theory"):
  with members:
    - "data processing theorems and the second law of thermodynamics"

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)