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: "tf replicator distributed machine learning for researchers"
  Abstract: We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i.e. one or many machines containing CPUs, GPUs or TPU accelerators) using synchronous or asynchronous training regimes. To demonstrate the generality and scalability of TF-Replicator, we implement and benchmark three very different models: (1) A ResNet-50 for ImageNet classification, (2) a SN-GAN for class-conditional ImageNet image generation, and (3) a D4PG reinforcement learning agent for continuous control. Our results show strong scalability performance without demanding any distributed systems expertise of the user. The TF-Replicator programming model will be open-sourced as part of TensorFlow 2.0 (see this https URL).

Connected through hyperedge HE-1 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "dance dance generation motion transfer for internet videos"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "pose guided human video generation"

Connected through hyperedge HE-2 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"
    - "learning to read by spelling towards unsupervised text recognition"

Connected through hyperedge HE-3 (induced by source paper "regularized hierarchical policies for compositional transfer in robotics"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "multitask soft option learning"
    - "dac the double actor critic architecture for learning options"
    - "information asymmetry in kl regularized rl"
    - "the termination critic"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "generating wikipedia by summarizing long sequences"
    - "differentiable plasticity training plastic neural networks with backpropagation"

Connected through hyperedge HE-5 (induced by source paper "large scale adversarial representation learning"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "variance reduction for policy gradient with action dependent factorized baselines"

Connected through hyperedge HE-7 (induced by source paper "non differentiable supervised learning with evolution strategies and hybrid methods"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural audio synthesis"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "don t decay the learning rate increase the batch size"
    - "deep rewiring training very sparse deep networks"

Connected through hyperedge HE-8 (induced by source paper "deep learning without weight transport"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "direct feedback alignment with sparse connections for local learning"
    - "loss landscapes of regularized linear autoencoders"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "deep residual learning for image recognition"

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)