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: "three for one and one for three flow segmentation and surface normals"
  Abstract: Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination. We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features. To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmentation, optical flow, and surface normals. The experimental results show positive influence among the three modalities, especially for objects' boundaries, region consistency, and scene structures.

Connected through hyperedge HE-1 (induced by source paper "360 o surface regression with a hyper sphere loss"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "learning to predict indoor illumination from a single image"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (induced by source paper "unsupervised generation of optical flow datasets"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "learning human optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "accurate optical flow via direct cost volume processing"
    - "the 2017 davis challenge on video object segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "im2flow motion hallucination from static images for action recognition"

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)