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 equivariant 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 systematically to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning system 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 a disjoint domain.

Connected through hyperedge HE-1 (induced by source paper "learning spatial common sense with geometry aware recurrent networks"):
  with members:
    - "fusion volumetric object level slam"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-2 (induced by source paper "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "context discovery for model learning in partially observable environments"
    - "autonomous grounding of visual field experience through sensorimotor prediction"
    - "the curious robot learning visual representations via physical interactions"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "you only look once unified real time object detection"
    - "grounding object perception in a naive agent s sensorimotor experience"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "shuffle and learn unsupervised learning using temporal order verification"):
  with members:
    - "slow feature analysis for human action recognition"
    - "generative image modeling using style and structure adversarial networks"
    - "unsupervised visual representation learning by context prediction"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised learning of visual representations using videos"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning image representations tied to ego motion"

Connected through hyperedge HE-4 (induced by source paper "convolutional patch representations for image retrieval an unsupervised approach"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "unsupervised feature learning from temporal data"
    - "learning image representations tied to ego motion"
    - "learning to see by moving"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "exploiting local features from deep networks for image retrieval"
    - "learning to compare image patches via convolutional neural networks"
    - "aggregating deep convolutional features for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "object centric representation learning from unlabeled videos"):
  with members:
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised feature learning from temporal data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning image representations tied to ego motion"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-6 (induced by source paper "walk and learn facial attribute representation learning from egocentric video and contextual data"):
  with members:
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised learning of visual representations using videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "improving image classification with location context"
    - "learning to see by moving"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-7 (induced by source paper "learning to extract motion from videos in convolutional neural networks"):
  with members:
    - "evaluating two stream cnn for video classification"
    - "spatial transformer networks"
    - "flownet learning optical flow with convolutional networks"
    - "learning image representations tied to ego motion"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "two stream convolutional networks for action recognition in videos"
    - "unsupervised learning of depth and motion"

Connected through hyperedge HE-8 (induced by source paper "egotransfer transferring motion across egocentric and exocentric domains using deep neural networks"):
  with members:
    - "egocentric meets surveillance"
    - "detecting engagement in egocentric video"
    - "ego2top matching viewers in egocentric and top view videos"
    - "compact cnn for indexing egocentric videos"
    - "deep residual learning for image recognition"
    - "learning spatiotemporal features with 3d 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)