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: "on variational bounds of mutual information"
  Abstract: Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning.

Connected through hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "entropy and mutual information in models of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning efficient representation for intrinsic motivation"
    - "learning representations in reinforcement learning an information bottleneck approach"
    - "unsupervised visuomotor control through distributional planning networks"
    - "the limits and potentials of deep learning for robotics"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "particle filter networks with application to visual localization"
    - "unifying map and landmark based representations for visual navigation"

Connected through hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "self training for end to end speech recognition"
    - "libri light a benchmark for asr with limited or no supervision"
    - "learning representations by maximizing mutual information across views"
    - "vq wav2vec self supervised learning of discrete speech representations"

Connected through hyperedge HE-4 (induced by source paper "learning unbiased representations via mutual information backpropagation"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "beyond local nash equilibria for adversarial networks"
    - "invariant representations without adversarial training"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"
    - "generalizing across domains via cross gradient training"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-6 (induced by source paper "think global act local relating dnn generalisation and node level snr"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "tightening mutual information based bounds on generalization error"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "entropy and mutual information in models of deep neural networks"
    - "putting an end to end to end gradient isolated learning of representations"
    - "chaining mutual information and tightening generalization bounds"
    - "theory iiib generalization in deep networks"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "prediction consistency curvature representation learning for locally linear control"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "learning latent dynamics for planning from pixels"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-8 (induced by source paper "unsupervised semantic attribute discovery and control in generative models"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "coco gan generation by parts via conditional coordinating"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "improved techniques for training gans"

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)