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: "improving spatiotemporal self supervision by deep reinforcement learning"
  Abstract: Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly address ordering of visual data in the spatial and temporal domain. The permutations of training samples, which are at the core of self-supervision by ordering, have so far been sampled randomly from a fixed preselected set. Based on deep reinforcement learning we propose a sampling policy that adapts to the state of the network, which is being trained. Therefore, new permutations are sampled according to their expected utility for updating the convolutional feature representation. Experimental evaluation on unsupervised and transfer learning tasks demonstrates competitive performance on standard benchmarks for image and video classification and nearest neighbor retrieval.

Connected through hyperedge HE-1 (induced by source paper "self supervised spatio temporal representation learning using variable playback speed prediction"):
  with members:
    - "video cloze procedure for self supervised spatio temporal learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "unsupervised domain adaptation through self supervision"
    - "self supervised gans via auxiliary rotation loss"
    - "group normalization"
    - "unsupervised representation learning by predicting image rotations"
    - "slowfast networks for video recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-2 (induced by source paper "video cloze procedure for self supervised spatio temporal learning"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised video representation learning with space time cubic puzzles"
    - "transitive invariance for self supervised visual representation learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "colorization as a proxy task for visual understanding"
    - "look listen and learn"

Connected through hyperedge HE-3 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "a survey of multi view representation learning"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "self supervised learning of video induced visual invariances"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "contrastive multiview coding"
    - "a systematic framework for natural perturbations from videos"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "self supervised learning via conditional motion propagation"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning by cross modal audio video clustering"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "self supervised learning for video correspondence flow"
    - "learning correspondence from the cycle consistency of time"
    - "self supervised audio visual co segmentation"
    - "video representation learning by dense predictive coding"
    - "randomly weighted cnns for music audio classification"
    - "scaling egocentric vision the epic kitchens dataset"

Connected through hyperedge HE-6 (induced by source paper "learning to train with synthetic humans"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning human optical flow"
    - "image based synthesis for deep 3d human pose estimation"
    - "learning to refine human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "divide and conquer the embedding space for metric learning"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep unsupervised learning of visual similarities"

Connected through hyperedge HE-8 (induced by source paper "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "sfa gtm seismic facies analysis based on generative topographic map and rbf"
    - "automatic salt deposits segmentation a deep learning approach"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep unsupervised learning of visual similarities"
    - "unsupervised representation learning by sorting sequences"
    - "deep unsupervised similarity learning using partially ordered sets"

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.CV(Computer Vision and Pattern Recognition)