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: "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
  Abstract: Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluation of scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

Connected through hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "depth coefficients for depth completion"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "hierarchical deep stereo matching on high resolution images"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "atari head atari human eye tracking and demonstration dataset"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "free lunch saliency via attention in atari agents"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"

Connected through hyperedge HE-3 (induced by source paper "don t forget the past recurrent depth estimation from monocular video"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "dynamonet dynamic action and motion network"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-4 (induced by source paper "learning depth via interaction"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-5 (induced by source paper "learning multi human optical flow"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning human optical flow"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-6 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "multi scale geometric consistency guided multi view stereo"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"

Connected through hyperedge HE-7 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation"):
  with members:
    - "dsgn deep stereo geometry network for 3d object detection"
    - "canadian adverse driving conditions dataset"
    - "object centric stereo matching for 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pv rcnn point voxel feature set abstraction for 3d object detection"
    - "task aware monocular depth estimation for 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "scalability in perception for autonomous driving waymo open dataset"

Connected through hyperedge HE-8 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learned collaborative stereo refinement"
    - "learning depth with convolutional spatial propagation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

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)