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: "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"
  Abstract: 3D scene understanding from images is a challenging problem which is encountered in robotics, augmented reality and autonomous driving scenarios. In this paper, we propose a novel approach to jointly infer the 3D rigid-body poses and shapes of vehicles from stereo images of road scenes. Unlike previous work that relies on geometric alignment of shapes with dense stereo reconstructions, our approach works directly on images and infers shape and pose efficiently through combined photometric and silhouette alignment of 3D shape priors with a stereo image. We use a shape prior that represents cars in a low-dimensional linear embedding of volumetric signed distance functions. For efficiently measuring the consistency with both alignment terms, we propose an adaptive sparse point selection scheme. In experiments, we demonstrate superior performance of our method in pose estimation and shape reconstruction over a state-of-the-art approach that uses geometric alignment with dense stereo reconstructions. Our approach can also boost the performance of deep-learning based approaches to 3D object detection as a refinement method. We demonstrate that our method significantly improves accuracy for several recent detection approaches.

Connected through hyperedge HE-1 (induced by source paper "zoomnet part aware adaptive zooming neural network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"
    - "densefusion 6d object pose estimation by iterative dense fusion"

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)