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: "strong data processing inequalities for channels and bayesian networks"
  Abstract: The data-processing inequality, that is, I(U; Y ) ≤ I(U; X) for a Markov chain U → X → Y, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. Various channel-dependent improvements (called strong data-processing inequalities, or SDPIs) of this inequality have been proposed both classically and more recently. In this note we first survey known results relating various notions of contraction for a single channel. Then we consider the basic extension: given SDPI for each constituent channel in a Bayesian network, how to produce an end-to-end SDPI?

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "broadcasting on random directed acyclic graphs"):
  with members:
    - "reliable computation by formulas of noisy and gates and noiseless xor gates with applications to quantum mechanics"
    - "noise thresholds for amplification quantum foundations from classical fault tolerant computation"
    - "dissipation of information in channels with input constraints"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "noise threshold for universality of 2 input gates"

Connected through hyperedge HE-3 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"
    - "on the emergence of invariance and disentangling in deep representations"

Connected through hyperedge HE-4 (induced by source paper "on data processing and majorization inequalities for f divergences with applications"):
  with members:
    - "minimum entropy couplings and their applications"
    - "on f divergences integral representations local behavior and inequalities"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "f divergence inequalities"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"
    - "strong data processing inequalities for input constrained additive noise channels"

Connected through hyperedge HE-5 (induced by source paper "low density majority codes and the problem of graceful degradation"):
  with members:
    - "a lower bound on the expected distortion of joint source channel coding"
    - "low complexity concatenated ldpc staircase codes"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "deep joint source channel coding for wireless image transmission"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "protograph based raptor like ldpc codes"
    - "intermediate performance of rateless codes"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

Connected through hyperedge HE-6 (induced by source paper "learnability for the information bottleneck"):
  with members:
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep variational information bottleneck"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "communication complexity of estimating correlations"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "secret key generation with limited interaction"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "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"
    - "converses for secret key agreement and secure computing"
    - "communication with imperfectly shared randomness"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-8 (induced by source paper "unifying the brascamp lieb inequality and the entropy power inequality"):
  with members:
    - "yet another proof of the entropy power inequality"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "superposition coding is almost always optimal for the poisson broadcast channel"
    - "an extremal inequality for long markov chains"
    - "information theoretic proofs of entropy power inequalities"

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)