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: "understanding the limitations of variational mutual information estimators"
  Abstract: Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE exhibit variance that could grow exponentially with the true amount of underlying MI. We also empirically demonstrate that existing estimators fail to satisfy basic self-consistency properties of MI, such as data processing and additivity under independence. Based on a unified perspective of variational approaches, we develop a new estimator that focuses on variance reduction. Empirical results on standard benchmark tasks demonstrate that our proposed estimator exhibits improved bias-variance trade-offs on standard benchmark tasks.

Connected through hyperedge HE-1 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting"):
  with members:
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a theory of usable information under computational constraints"):
  with members:
    - "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"
    - "information theoretical estimators toolbox"

Connected through hyperedge HE-3 (induced by source paper "reliable estimation of kullback leibler divergence by controlling discriminator complexity in the reproducing kernel hilbert space"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "amortised map inference for image super resolution"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "information condensing active learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "emnist an extension of mnist to handwritten letters"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "ccmi classifier based conditional mutual information estimation"
    - "a brief introduction to machine learning for engineers"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

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)