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: "wasserstein dependency measure for representation learning"
  Abstract: Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge.

Connected through hyperedge HE-1 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a plug in method for representation factorization"):
  with members:
    - "seeing what a gan cannot generate"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural voice cloning with a few samples"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "pioneer networks progressively growing generative autoencoder"

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"
    - "learning representations by maximizing mutual information across views"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-4 (induced by source paper "fast task inference with variational intrinsic successor features"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "model based reinforcement learning for atari"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "universal successor features approximators"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "understanding the limitations of variational mutual information estimators"):
  with members:
    - "on mutual information maximization for representation learning"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "formal limitations on the measurement of mutual information"
    - "towards a definition of disentangled representations"
    - "mine mutual information neural estimation"
    - "bias and generalization in deep generative models an empirical study"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "disentangled representation learning with wasserstein total correlation"):
  with members:
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "towards a definition of disentangled representations"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "unsupervised learning of disentangled representations from video"
    - "emergence of invariance and disentanglement in deep representations"
    - "improved training of wasserstein 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)