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: "a note on the strong data processing inequalities in bayesian networks"
  Abstract: The data-processing inequality, that is, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. Various channel-dependent strengthenings of this inequality have been proposed both classically and more recently. This note considers the basic information-theoretic question: given strong data-processing inequality (SDPI) for each constituent channel in a Bayesian network, how does one produce an end-to-end SDPI? #R##N#Our approach is based on the (extract of the) Evans-Schulman method, which is demonstrated for three different kinds of SDPIs, namely, the usual Ahslwede-G\'acs type contraction coefficients (mutual information), Dobrushin's contraction coefficients (total variation), and finally the $F_I$-curve (the best possible SDPI for a given channel). As an example, it is shown how to obtain SDPI for an $n$-letter memoryless channel with feedback given an SDPI for $n=1$.

Connected through hyperedge HE-1 (induced by source paper "comparison of channels criteria for domination by a symmetric channel"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "bounds between contraction coefficients"
    - "dissipation of information in channels with input constraints"
    - "universal polar codes for more capable and less noisy channels and sources"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "capacity regions of two new classes of 2 receiver broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "application of information percolation method to reconstruction problems on graphs"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "mutual information in rank one matrix estimation"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "dissipation of information in channels with input constraints"

Connected through hyperedge HE-3 (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"
    - "converses for distributed estimation via strong data processing inequalities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "on non interactive simulation of joint distributions"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

Connected through hyperedge HE-4 (induced by source paper "common randomness and key generation with limited interaction"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "converses for secret key agreement and secure computing"
    - "secret key agreement general capacity and second order asymptotics"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "outer bounds for multiterminal source coding via a strong data processing inequality"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

Connected through hyperedge HE-5 (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"
    - "converses for distributed estimation via strong data processing inequalities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "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-6 (induced by source paper "information theoretic lower bounds on bayes risk in decentralized estimation"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "are slepian wolf rates necessary for distributed parameter estimation"
    - "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"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "distance based and continuum fano inequalities with applications to statistical estimation"

Connected through hyperedge HE-7 (induced by source paper "information theoretic lower bounds for distributed function computation"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"
    - "rate region of the quadratic gaussian two encoder source coding problem"

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)