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: "learning features by watching objects move"
  Abstract: This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.

Connected through hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "motion attentive transition for zero shot video object segmentation"
    - "self supervised learning for video correspondence flow"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "multi source weak supervision for saliency detection"
    - "learning correspondence from the cycle consistency of time"

Connected through hyperedge HE-2 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-4 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "geometry aware generation of adversarial and cooperative point clouds"
    - "orthogonal deep neural networks"
    - "learning graph embedding with adversarial training methods"
    - "revisiting self supervised visual representation learning"
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "pruning convolutional neural networks with self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "contrastive multiview coding"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-6 (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"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "joint encoding of appearance and motion features with self supervision for first person action recognition"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "lsta long short term attention for egocentric action recognition"
    - "audio visual scene analysis with self supervised multisensory features"
    - "representation learning by learning to count"
    - "online action detection"

Connected through hyperedge HE-8 (induced by source paper "towards accurate vehicle behaviour classification with multi relational graph convolutional networks"):
  with members:
    - "dynamic traffic scene classification with space time coherence"
    - "learning category specific mesh reconstruction from image collections"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "attention is all you need"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "semi supervised classification with graph convolutional networks"

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)