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: "fast multi frame stereo scene flow with motion segmentation"
  Abstract: We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks &#x2013; stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [30], which is currently ranked second on the KITTI benchmark.

Connected through hyperedge HE-1 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"):
  with members:
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "deep rigid instance scene flow"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "deep visual domain adaptation a survey"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "probflow joint optical flow and uncertainty estimation"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-2 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge"):
  with members:
    - "learning to adapt for stereo"
    - "sense a shared encoder network for scene flow estimation"
    - "guided stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "real time semantic stereo matching"
    - "deep rigid instance scene flow"

Connected through hyperedge HE-3 (induced by source paper "learning independent object motion from unlabelled stereoscopic videos"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-4 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "learning a discriminative prior for blind image deblurring"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"

Connected through hyperedge HE-5 (induced by source paper "pwoc 3d deep occlusion aware end to end scene flow estimation"):
  with members:
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-6 (induced by source paper "stereo based multi motion visual odometry for mobile robots"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "effective sampling fast segmentation using robust geometric model fitting"
    - "moving object tracking employing rigid body motion on matrix lie groups"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mot16 a benchmark for multi object tracking"
    - "direct sparse odometry"

Connected through hyperedge HE-7 (induced by source paper "geonet unsupervised learning of dense depth optical flow and camera pose"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "optical flow in mostly rigid scenes"
    - "topometric localization with deep learning"
    - "unsupervised learning of depth and ego motion from video"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "dense scene flow from stereo disparity and optical flow"):
  with members:
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "a continuous optimization approach for efficient and accurate scene flow"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"

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)