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: "cross and learn cross modal self supervision"
  Abstract: In this paper we present a self-supervised method for representation learning utilizing two different modalities. Based on the observation that cross-modal information has a high semantic meaning we propose a method to effectively exploit this signal. For our approach we utilize video data since it is available on a large scale and provides easily accessible modalities given by RGB and optical flow. We demonstrate state-of-the-art performance on highly contested action recognition datasets in the context of self-supervised learning. We show that our feature representation also transfers to other tasks and conduct extensive ablation studies to validate our core contributions.

Connected through hyperedge HE-1 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "charting the right manifold manifold mixup for few shot learning"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-5 (induced by source paper "revisiting self supervised visual representation learning"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "self supervised learning of a facial attribute embedding from video"
    - "deep clustering for unsupervised learning of visual features"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "invertible residual networks"

Connected through hyperedge HE-6 (induced by source paper "s4l self supervised semi supervised learning"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-7 (induced by source paper "mic mining interclass characteristics for improved metric learning"):
  with members:
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep metric learning with hierarchical triplet loss"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-8 (induced by source paper "self supervised spatiotemporal feature learning via video rotation prediction"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "real world anomaly detection in surveillance videos"
    - "moments in time dataset one million videos for event understanding"
    - "videocapsulenet a simplified network for action detection"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised video representation learning with space time cubic puzzles"

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)