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 image representations tied to ego motion"
  Abstract: Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance i.e. they respond predictably to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning approach significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in static images from a disjoint domain.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "multigrid predictive filter flow for unsupervised learning on videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep clustering for unsupervised learning of visual features"
    - "efficient video object segmentation via network modulation"
    - "object discovery in videos as foreground motion clustering"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (induced by source paper "scaling and benchmarking self supervised visual representation learning"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "gibson env real world perception for embodied agents"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "learning to separate object sounds by watching unlabeled video"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "data efficient image recognition with contrastive predictive coding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-5 (induced by source paper "learning correspondence from the cycle consistency of time"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep clustering for unsupervised learning of visual features"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "oops predicting unintentional action in video"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "self supervised learning for video correspondence flow"
    - "gaze360 physically unconstrained gaze estimation in the wild"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "learning representations for predicting future activities"
    - "revisiting self supervised visual representation learning"
    - "back to the future knowledge distillation for human action anticipation"

Connected through hyperedge HE-7 (induced by source paper "embodied visual recognition"):
  with members:
    - "learning instance segmentation by interaction"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "neural modular control for embodied question answering"

Connected through hyperedge HE-8 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

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)