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: "object centric photometric bundle adjustment with deep shape prior"
  Abstract: Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve this problem through projected point displacement \& bundle adjustment. More recently, deep methods have attempted to solve this problem by directly learning a relationship between geometry and appearance. There is, however, a significant gap between these two strategies. SfM tackles the problem from purely a geometric perspective, taking no account of the object shape prior. Modern deep methods more often throw away geometric constraints altogether, rendering the results unreliable. In this paper we make an effort to bring these two seemingly disparate strategies together. We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.

Connected through hyperedge HE-1 (induced by source paper "meshlet priors for 3d mesh reconstruction"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-2 (induced by source paper "multiview aggregation for learning category specific shape reconstruction"):
  with members:
    - "equivariant multi view networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "photometric mesh optimization for video aligned 3d object reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "proxy templates for inverse compositional photometric bundle adjustment"
    - "unsupervised learning of depth and ego motion from video"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-4 (induced by source paper "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"):
  with members:
    - "panoptic segmentation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "ldso direct sparse odometry with loop closure"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "pyramid stereo matching network"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "integrating objects into monocular slam line based category specific models"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "edge slam edge points based monocular visual slam"
    - "slam with objects using a nonparametric pose graph"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "specular to diffuse translation for multi view reconstruction"):
  with members:
    - "lime live intrinsic material estimation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "perceptual adversarial networks for image to image transformation"
    - "superpoint self supervised interest point detection and description"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "single view object shape reconstruction using deep shape prior and silhouette"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-8 (induced by source paper "semantic photometric bundle adjustment on natural sequences"):
  with members:
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

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)