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: "conditional prior networks for optical flow"
  Abstract: Classical computation of optical flow involves generic priors (regularizers) that capture rudimentary statistics of images, but not long-range correlations or semantics. On the other hand, fully supervised methods learn the regularity in the annotated data, without explicit regularization and with the risk of overfitting. We seek to learn richer priors on the set of possible flows that are statistically compatible with an image. Once the prior is learned in a supervised fashion, one can easily learn the full map to infer optical flow directly from two or more images, without any need for (additional) supervision. We introduce a novel architecture, called Conditional Prior Network (CPN), and show how to train it to yield a conditional prior. When used in conjunction with a simple optical flow architecture, the CPN beats all variational methods and all unsupervised learning-based ones using the same data term. It performs comparably to fully supervised ones, that however are fine-tuned to a particular dataset. Our method, on the other hand, performs well even when transferred between datasets.

Connected through hyperedge HE-1 (induced by source paper "dense depth posterior ddp from single image and sparse range"):
  with members:
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sbnet sparse blocks network for fast inference"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

Connected through hyperedge HE-2 (induced by source paper "geonet deep geodesic networks for point cloud analysis"):
  with members:
    - "dense depth posterior ddp from single image and sparse range"
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "geo supervised visual depth prediction"
    - "pcpnet learning local shape properties from raw point clouds"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "unsupervised moving object detection via contextual information separation"):
  with members:
    - "learning video object segmentation with visual memory"
    - "segflow joint learning for video object segmentation and optical flow"
    - "learning motion patterns in videos"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "shape tracking with occlusions via coarse to fine region based sobolev descent"

Connected through hyperedge HE-4 (induced by source paper "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "dense depth posterior ddp from single image and sparse range"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sbnet sparse blocks network for fast inference"
    - "sparse and dense data with cnns depth completion and semantic 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)