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 theoretical estimators toolbox"
  Abstract: We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.

Connected through hyperedge HE-1 (induced by source paper "a theory of usable information under computational constraints"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "on variational bounds of mutual information"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "representation learning with contrastive predictive coding"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "estimating mutual information for discrete continuous mixtures"
    - "censoring representations with an adversary"

Connected through hyperedge HE-2 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-3 (induced by source paper "shredder learning noise to protect privacy with partial dnn inference on the edge"):
  with members:
    - "zombieload cross privilege boundary data sampling"
    - "chiron privacy preserving machine learning as a service"
    - "spectre attacks exploiting speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-4 (induced by source paper "generative modeling using the sliced wasserstein distance"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "progressive growing of gans for improved quality stability and variation"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"
    - "stacked generative adversarial networks"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "the radon cumulative distribution transform and its application to image classification"

Connected through hyperedge HE-5 (induced by source paper "direct estimation of density functionals using a polynomial basis"):
  with members:
    - "improving convergence of divergence functional ensemble estimators"
    - "minimax estimation of functionals of discrete distributions"
    - "multivariate f divergence estimation with confidence"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "kernels on sample sets via nonparametric divergence estimates"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-6 (induced by source paper "adaptive mcmc via combining local samplers"):
  with members:
    - "adaptive monte carlo via bandit allocation"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "adversarial feature learning"
    - "improved generator objectives for gans"
    - "pixel recurrent neural networks"
    - "on the quantitative analysis of decoder based generative models"

Connected through hyperedge HE-8 (induced by source paper "estimating mutual information for discrete continuous mixtures"):
  with members:
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

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)