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: "mean estimation from one bit measurements"
  Abstract: We consider the problem of estimating the mean of a symmetric log-concave distribution under the following constraint: only a single bit per sample from this distribution is available to the estimator. We study the mean squared error (MSE) risk in this estimation as a function of the number of samples, and hence the number of bits, from this distribution. Under an adaptive setting in which each bit is a function of the current sample and the previously observed bits, we show that the optimal relative efficiency compared to the sample mean is the efficiency of the median. For example, in estimating the mean of a normal distribution, a constraint of one bit per sample incurs a penalty of $\pi/2$ in sample size compared to the unconstrained case. We also consider a distributed setting where each one-bit message is only a function of a single sample. We derive lower bounds on the MSE in this setting, and show that the optimal efficiency can only be attained at a finite number of points in the parameter space. Finally, we analyze a distributed setting where the bits are obtained by comparing each sample against a prescribed threshold. Consequently, we consider the threshold density that minimizes the maximal MSE. Our results indicate that estimating the mean from one-bit measurements is equivalent to estimating the sample median from these measurements. In the adaptive case, this estimate can be done with vanishing error for any point in the parameter space. In the distributed case, this estimate can be done with vanishing error only for a finite number of possible values for the unknown mean.

Connected through hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"
    - "the rate distortion risk in estimation from compressed data"

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)