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: "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
  Abstract: Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients. We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from \emph{raw} video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "high fidelity synthesis with disentangled representation"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "weakly supervised disentanglement by pairwise similarities"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-3 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "grasp2vec learning object representations from self supervised grasping"
    - "residual reinforcement learning for robot control"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "machine teaching a new paradigm for building machine learning systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "phase transitions for the information bottleneck in representation learning"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep variational information bottleneck"

Connected through hyperedge HE-5 (induced by source paper "significance aware information bottleneck for domain adaptive semantic segmentation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-6 (induced by source paper "hindsight generative adversarial imitation learning"):
  with members:
    - "generative adversarial self imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "zero shot visual imitation"
    - "curiosity driven experience prioritization via density estimation"
    - "self imitation learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "generative adversarial imitation from observation"

Connected through hyperedge HE-7 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"

Connected through hyperedge HE-8 (induced by source paper "positive unlabeled reward learning"):
  with members:
    - "reinforced imitation in heterogeneous action space"
    - "task relevant adversarial imitation learning"
    - "a framework for data driven robotics"
    - "deepmind control suite"
    - "classification from positive unlabeled and biased negative data"
    - "sample efficient imitation learning via generative adversarial nets"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "distributed prioritized experience replay"

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)