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: "optimal identical binary quantizer design for distributed estimation"
  Abstract: We consider the design of identical one-bit probabilistic quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Cramer-Rao lower bound (CRB) over the parameter-range as our performance metric. We restrict our theoretical analysis to the class of antisymmetric quantizers and determine a set of conditions for which the probabilistic quantizer function is greatly simplified. We identify a broad class of noise distributions, which includes Gaussian noise in the low-SNR regime, for which the often used threshold-quantizer is found to be minimax-optimal. Aided with theoretical results, we formulate an optimization problem to obtain the optimum minimax-CRB quantizer. For a wide range of noise distributions, we demonstrate the superior performance of the new quantizer-particularly in the moderate to high-SNR regime.

Connected through hyperedge HE-1 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-2 (induced by source paper "on bayesian fisher information maximization for distributed vector estimation"):
  with members:
    - "distributed vector estimation for power and bandwidth constrained wireless sensor networks"
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "distributed estimation of a parametric field algorithms and performance analysis"

Connected through hyperedge HE-3 (induced by source paper "optimized sensor collaboration for estimation of temporally correlated parameters"):
  with members:
    - "network topology adaptation and interference coordination for energy saving in heterogeneous networks"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "optimal power allocation for distributed blue estimation with linear spatial collaboration"
    - "distributed estimation and detection with bounded transmissions over gaussian multiple access channels"
    - "controlled collaboration for linear coherent estimation in wireless sensor networks"
    - "on linear coherent estimation with spatial collaboration"
    - "distributed detection over fading macs with multiple antennas at the fusion center"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-4 (induced by source paper "distributed vector estimation for power and bandwidth constrained wireless sensor networks"):
  with members:
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "distributed estimation of a parametric field algorithms and performance analysis"
    - "the effect of correlated observations on the performance of distributed estimation"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-5 (induced by source paper "on linear coherent estimation with spatial collaboration"):
  with members:
    - "spatial whitening framework for distributed estimation"
    - "cooperative transmission for relay networks based on second order statistics of channel state information"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"
    - "network beamforming using relays with perfect channel information"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-6 (induced by source paper "controlled collaboration for linear coherent estimation in wireless sensor networks"):
  with members:
    - "cramer rao bounds for polynomial signal estimation using sensors with ar 1 drift"
    - "on linear coherent estimation with spatial collaboration"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"
    - "estimation diversity and energy efficiency in distributed sensing"

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)