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: "converses for distributed estimation via strong data processing inequalities"
  Abstract: We consider the problem of distributed estimation, where local processors observe independent samples conditioned on a common random parameter of interest, map the observations to a finite number of bits, and send these bits to a remote estimator over independent noisy channels. We derive converse results for this problem, such as lower bounds on Bayes risk. The main technical tools include a lower bound on the Bayes risk via mutual information and small ball probability, as well as strong data processing inequalities for the relative entropy. Our results can recover and improve some existing results on distributed estimation with noiseless channels, and also capture the effect of noisy channels on the estimation performance.

Connected through hyperedge HE-1 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse"):
  with members:
    - "arimoto r enyi conditional entropy and bayesian m ary hypothesis testing"
    - "strengthening the entropy power inequality"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "on non interactive simulation of joint distributions"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

Connected through hyperedge HE-2 (induced by source paper "strong data processing inequalities for channels and bayesian networks"):
  with members:
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "on the entropy of a noisy function"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-3 (induced by source paper "brascamp lieb inequality and its reverse an information theoretic view"):
  with members:
    - "information theoretic perspectives on brascamp lieb inequality and its reverse"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "an extremal inequality for long markov chains"
    - "information theoretic applications of the logarithmic probability comparison bound"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "wyner s common information generalizations and a new lossy source coding interpretation"

Connected through hyperedge HE-4 (induced by source paper "a note on the strong data processing inequalities in bayesian networks"):
  with members:
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "bounds between contraction coefficients"
    - "on the entropy of a noisy function"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-5 (induced by source paper "are slepian wolf rates necessary for distributed parameter estimation"):
  with members:
    - "learning from compressed observations"

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)