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: "practical deep stereo pds toward applications friendly deep stereo matching"
  Abstract: End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel cross- entropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning energy based inpainting for optical flow"

Connected through hyperedge HE-2 (induced by source paper "resdepth learned residual stereo reconstruction"):
  with members:
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "dsm building shape refinement from combined remote sensing images based on wnet cgans"
    - "deepmvs learning multi view stereopsis"
    - "pyramid stereo matching network"
    - "semantically informed multiview surface refinement"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "real time semantic stereo matching"):
  with members:
    - "residual pyramid learning for single shot semantic segmentation"
    - "guided stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "guided upsampling network for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "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"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-5 (induced by source paper "edgestereo an effective multi task learning network for stereo matching and edge detection"):
  with members:
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "left right comparative recurrent model for stereo matching"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "pyramid stereo matching network"
    - "a general and adaptive robust loss function"
    - "self supervised learning for stereo matching with self improving ability"

Connected through hyperedge HE-6 (induced by source paper "dpsnet end to end deep plane sweep stereo"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "robust depth estimation from auto bracketed images"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-7 (induced by source paper "amnet deep atrous multiscale stereo disparity estimation networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "segstereo exploiting semantic information for disparity estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid stereo matching network"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "group wise correlation stereo network"):
  with members:
    - "feature intertwiner for object detection"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "open world stereo video matching with deep rnn"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "edgestereo a context integrated residual pyramid network for stereo matching"

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)