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: "amnet deep atrous multiscale stereo disparity estimation networks"
  Abstract: In this paper, a new deep learning architecture for stereo disparity estimation is proposed. The proposed atrous multiscale network (AMNet) adopts an efficient feature extractor with depthwise-separable convolutions and an extended cost volume that deploys novel stereo matching costs on the deep features. A stacked atrous multiscale network is proposed to aggregate rich multiscale contextual information from the cost volume which allows for estimating the disparity with high accuracy at multiple scales. AMNet can be further modified to be a foreground-background aware network, FBA-AMNet, which is capable of discriminating between the foreground and the background objects in the scene at multiple scales. An iterative multitask learning method is proposed to train FBA-AMNet end-to-end. The proposed disparity estimation networks, AMNet and FBA-AMNet, show accurate disparity estimates and advance the state of the art on the challenging Middlebury, KITTI 2012, KITTI 2015, and Sceneflow stereo disparity estimation benchmarks.

Connected through hyperedge HE-1 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "single image depth estimation by dilated deep residual convolutional neural network and soft weight sum inference"

Connected through hyperedge HE-2 (induced by source paper "keypose multi view 3d labeling and keypoint estimation for transparent objects"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "point based multi view stereo network"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"

Connected through hyperedge HE-3 (induced by source paper "deep robust single image depth estimation neural network using scene understanding"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"

Connected through hyperedge HE-4 (induced by source paper "depth agmnet an atrous granular multiscale stereo network based on depth edge auxiliary task"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "group wise correlation stereo network"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "self supervised learning for stereo matching with self improving ability"

Connected through hyperedge HE-5 (induced by source paper "dedge agmnet a robust multi task learning network for stereo matching and depth edge detection"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "group wise correlation stereo network"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "self supervised learning for stereo matching with self improving ability"

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)