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: "guided optical flow learning"
  Abstract: We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned. We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning. The models are further refined in an unsupervised fashion using an image reconstruction loss. Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time.

Connected through hyperedge HE-1 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "segmentation aware convolutional networks using local attention masks"
    - "optical flow in mostly rigid scenes"

Connected through hyperedge HE-2 (induced by source paper "self supervised monocular depth hints"):
  with members:
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "learning depth with convolutional spatial propagation network"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"

Connected through hyperedge HE-3 (induced by source paper "neurreg neural registration and its application to image segmentation"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "neural multi scale self supervised registration for echocardiogram dense tracking"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-4 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "image de raining using a conditional generative adversarial network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-5 (induced by source paper "learning optical flow via dilated networks and occlusion reasoning"):
  with members:
    - "video enhancement with task oriented flow"
    - "hidden two stream convolutional networks for action recognition"
    - "occlusion aware unsupervised learning of optical flow"
    - "dilated residual networks"
    - "densenet for dense flow"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-6 (induced by source paper "optical flow super resolution based on image guidence using convolutional neural network"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "residual dense network for image super resolution"
    - "accurate optical flow via direct cost volume processing"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "accelerating the super resolution convolutional neural network"
    - "is the deconvolution layer the same as a convolutional layer"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "ev flownet self supervised optical flow estimation for event based cameras"):
  with members:
    - "abmof a novel optical flow algorithm for dynamic vision sensors"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "stacked hourglass networks for human pose estimation"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "u net convolutional networks for biomedical image segmentation"
    - "bioinspired visual motion estimation"

Connected through hyperedge HE-8 (induced by source paper "lucid data dreaming for video object segmentation"):
  with members:
    - "deep spatio temporal random fields for efficient video segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "super trajectory for video segmentation"
    - "learning video object segmentation with visual memory"
    - "learning from synthetic humans"
    - "online adaptation of convolutional neural networks for video object segmentation"

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)