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: "tighter variational representations of f divergences via restriction to probability measures"
  Abstract: We show that the variational representations for f-divergences currently used in the literature can be tightened. This has implications to a number of methods recently proposed based on this representation. As an example application we use our tighter representation to derive a general f-divergence estimator based on two i.i.d. samples and derive the dual program for this estimator that performs well empirically. We also point out a connection between our estimator and MMD.

Connected through hyperedge HE-1 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-2 (induced by source paper "novel change of measure inequalities and pac bayesian bounds"):
  with members:
    - "scalable information inequalities for uncertainty quantification"
    - "dependence measures bounding the exploration bias for general measurements"
    - "pac bayesian inequalities for martingales"

Connected through hyperedge HE-3 (induced by source paper "on mutual information maximization for representation learning"):
  with members:
    - "a theory of usable information under computational constraints"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "on variational bounds of mutual information"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "rethinking lossy compression the rate distortion perception tradeoff"

Connected through hyperedge HE-4 (induced by source paper "bridging the gap between f gans and wasserstein gans"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "learning implicit generative models with the method of learned moments"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the inductive bias of restricted f gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (induced by source paper "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  with members:
    - "policy optimization as wasserstein gradient flows"
    - "a convex duality framework for gans"
    - "adversarial distillation of bayesian neural network posteriors"
    - "on wasserstein reinforcement learning and the fokker planck equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "boosting the actor with dual critic"
    - "learning from conditional distributions via dual embeddings"

Connected through hyperedge HE-6 (induced by source paper "credible sample elicitation by deep learning for deep learning"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "incentives for truthful peer grading"

Connected through hyperedge HE-7 (induced by source paper "deep learning for channel coding via neural mutual information estimation"):
  with members:
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "mine mutual information neural estimation"
    - "an introduction to deep learning for the physical layer"
    - "demystifying fixed k nearest neighbor information estimators"
    - "adam a method for stochastic optimization"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-8 (induced by source paper "asymptotic finite sample information losses in neural classifiers"):
  with members:
    - "mine mutual information neural estimation"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "adam a method for stochastic optimization"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "active learning with statistical models"

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.LG(Machine Learning)