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 stereo matching with explicit cost aggregation sub architecture"
  Abstract: Deep neural networks have shown excellent performance for stereo matching. Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cost aggregation which is crucial for stereo matching. In this paper, we present a learning-based cost aggregation method for stereo matching by a novel sub-architecture in the end-to-end trainable pipeline. We reformulate the cost aggregation as a learning process of the generation and selection of cost aggregation proposals which indicate the possible cost aggregation results. The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals. The criterion for the selection is determined by the low-level structure information obtained from a light convolutional network. The two-stream network offers a global view guidance for the cost aggregation to rectify the mismatching value stemming from the limited view of the matching cost computation. The comprehensive experiments on challenge datasets such as KITTI and Scene Flow show that our method outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for depth prediction from images"):
  with members:
    - "learning depth with convolutional spatial propagation network"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "continuous 3d label stereo matching using local expansion moves"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-2 (induced by source paper "edgestereo an effective multi task learning network for stereo matching and edge detection"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "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-3 (induced by source paper "group wise correlation stereo network"):
  with members:
    - "feature intertwiner for object detection"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "open world stereo video matching with deep rnn"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of multi scale convolutional neural network for stereo matching"):
  with members:
    - "yolov3 an incremental improvement"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"

Connected through hyperedge HE-5 (induced by source paper "segstereo exploiting semantic information for disparity estimation"):
  with members:
    - "pyramid stereo matching network"
    - "a general and adaptive robust loss function"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "segflow joint learning for video object segmentation and optical flow"
    - "learning for disparity estimation through feature constancy"
    - "semi supervised deep learning for monocular depth map prediction"
    - "cascaded scene flow prediction using semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "edgestereo a context integrated residual pyramid network for stereo matching"):
  with members:
    - "segstereo exploiting semantic information for disparity estimation"
    - "pyramid stereo matching network"
    - "self supervised learning for stereo matching with self improving ability"
    - "what can help pedestrian detection"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "richer convolutional features for edge detection"
    - "segflow joint learning for video object segmentation and optical flow"
    - "learning for disparity estimation through feature constancy"

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)