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: "anytime stereo image depth estimation on mobile devices"
  Abstract: Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process 1242$ \times $375 resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error -- using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at this https URL .

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 "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"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-3 (induced by source paper "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "dense depth posterior ddp from single image and sparse range"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "a general pipeline for 3d detection of vehicles"

Connected through hyperedge HE-4 (induced by source paper "domain invariant stereo matching networks"):
  with members:
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "learning to adapt for stereo"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "semantic image synthesis with spatially adaptive normalization"
    - "real time self adaptive deep stereo"
    - "segstereo exploiting semantic information for disparity estimation"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-5 (induced by source paper "alert accurate anytime learning for energy and timeliness"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "sparsely aggregated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep learning assisted high resolution binocular stereo depth reconstruction"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "real time dense depth estimation by fusing stereo with sparse depth measurements"
    - "superdepth self supervised super resolved monocular depth estimation"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-7 (induced by source paper "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "deep ordinal regression network for monocular depth estimation"
    - "pyramid stereo matching network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

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)