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: "connecting generative adversarial networks and actor critic methods"
  Abstract: Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training. Here we show that GANs can be viewed as actor-critic methods in an environment where the actor cannot affect the reward. We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. We also review a number of extensions to GANs and RL algorithms with even more complicated information flow. We hope that by highlighting this formal connection we will encourage both GAN and RL communities to develop general, scalable, and stable algorithms for multilevel optimization with deep networks, and to draw inspiration across communities.

Connected through hyperedge HE-1 (induced by source paper "feature map level online adversarial knowledge distillation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "federated learning challenges methods and future directions"
    - "paraphrasing complex network network compression via factor transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "ktan knowledge transfer adversarial network"
    - "deep mutual learning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-2 (induced by source paper "a unified analysis of gradient based methods for a whole spectrum of games"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-3 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-4 (induced by source paper "prnet self supervised learning for partial to partial registration"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "kpconv flexible and deformable convolution for point clouds"
    - "deep part induction from articulated object pairs"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "rethinking imagenet pre training"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"

Connected through hyperedge HE-5 (induced by source paper "tf replicator distributed machine learning for researchers"):
  with members:
    - "cgans with projection discriminator"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "high accuracy low precision training"
    - "spectral normalization for generative adversarial networks"
    - "distributed prioritized experience replay"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-6 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "embarrassingly shallow autoencoders for sparse data"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "deep learning based recommender system a survey and new perspectives"
    - "cgans with projection discriminator"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "neural temporal difference learning converges to global optima"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "td or not td analyzing the role of temporal differencing in deep reinforcement learning"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-8 (induced by source paper "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "one big net for everything"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "coulomb gans provably optimal nash equilibria via potential fields"

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)