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: "information theoretic lower bounds on bayes risk in decentralized estimation"
  Abstract: We derive lower bounds on the Bayes risk in decentralized estimation, where the estimator does not have direct access to the random samples generated conditionally on the random parameter of interest, but only to the data received from local processors that observe the samples. The received data are subject to communication constraints, due to quantization and the noise in the communication channels from the processors to the estimator. We first derive general lower bounds on the Bayes risk using information-theoretic quantities, such as mutual information, information density, small ball probability, and differential entropy. We then apply these lower bounds to the decentralized case, using strong data processing inequalities to quantify the contraction of information due to communication constraints. We treat the cases of a single processor and of multiple processors, where the samples observed by different processors may be conditionally dependent given the parameter, for noninteractive and interactive communication protocols. Our results recover and improve recent lower bounds on the Bayes risk and the minimax risk for certain decentralized estimation problems, where previously only conditionally independent sample sets and noiseless channels have been considered. Moreover, our results provide a general way to quantify the degradation of estimation performance caused by distributing resources to multiple processors, which is only discussed for specific examples in existing works.

Connected through hyperedge HE-1 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "vqsgd vector quantized stochastic gradient descent"
    - "distributed learning with sublinear communication"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "sparsified sgd with memory"

Connected through hyperedge HE-2 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "the optimality of correlated sampling"
    - "decidability of non interactive simulation of joint distributions"

Connected through hyperedge HE-3 (induced by source paper "finite precision stochastic optimization accounting for the bias"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "federated learning strategies for improving communication efficiency"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-4 (induced by source paper "mean estimation from one bit measurements"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "optimality guarantees for distributed statistical estimation"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for learning distributions under communication constraints via fisher information"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "distributed simulation and distributed inference"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "mutual information optimally local private discrete distribution estimation"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on communication cost of distributed statistical estimation and dimensionality"

Connected through hyperedge HE-6 (induced by source paper "learning of tree structured gaussian graphical models on distributed data under communication constraints"):
  with members:
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "optimality guarantees for distributed statistical estimation"
    - "learning high dimensional markov forest distributions analysis of error rates"
    - "learning from compressed observations"

Connected through hyperedge HE-7 (induced by source paper "distributed simulation and distributed inference"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "test without trust optimal locally private distribution testing"
    - "sample optimal identity testing with high probability"
    - "the optimality of correlated sampling"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"

Connected through hyperedge HE-8 (induced by source paper "inference under information constraints i lower bounds from chi square contraction"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "two party distribution testing communication and security"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "a general characterization of the statistical query complexity"

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)