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: "deep rigid instance scene flow"
  Abstract: In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. We formulate the problem as energy minimization in a deep structured model, which can be solved efficiently in the GPU by unrolling a Gaussian-Newton solver. Our experiments in the challenging KITTI scene flow dataset show that we outperform the state-of-the-art by a very large margin, while being 800 times faster.

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"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "deep visual domain adaptation a survey"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "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 "budgeted training rethinking deep neural network training under resource constraints"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learnable embedding space for efficient neural architecture compression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "gradient descent finds global minima of deep neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "darts differentiable architecture search"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "panoptic segmentation"
    - "video instance segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "sense a shared encoder network for scene flow estimation"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "unified perceptual parsing for scene understanding"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-5 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-6 (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"
    - "real time self adaptive deep stereo"

Connected through hyperedge HE-7 (induced by source paper "flownet3d geometric losses for deep scene flow estimation"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "pyramid stereo matching network"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"

Connected through hyperedge HE-8 (induced by source paper "self supervised object motion and depth estimation from video"):
  with members:
    - "learning independent object motion from unlabelled stereoscopic videos"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "signet semantic instance aided unsupervised 3d geometry perception"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"

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)