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: "seethrough finding chairs in heavily occluded indoor scenes"
  Abstract: 3D geometry mockups of single images of indoor scenes are useful for many applications including interior design, content creation for virtual reality, and image manipulation. Unfortunately, manually modeling a scene from a single image is tedious and requires expert knowledge. We aim to construct scene mockups from single images automatically. However, automatically inferring 3D scenes from 2D images is an ill-posed problem, as 2D images are projections of many meaningful attributes (e.g., geometry, illumination, material), with objects regularly being occluded by themselves or others. Recent systems targeting scene mockups mirror this observation and work mostly on fully visible objects. To improve the state-of-the-art we need to take into account contextual information, exploiting the fact that objects co-occur mostly in typical regular configurations. We exploit such regularity in the form of data-driven scene priors. Specifically, we first use a neural network trained on scene arrangement to extract keypoints, then feed these keypoints to a candidate object generation stage. From these candidates, an optimal selection is then made using pairwise co-occurrence statistics, discovered from a database of 3D scenes. We iterate the process allowing for candidates with low keypoint response to be detected based on the location of neighbouring objects. We demonstrate that our pipeline outperforms combinations of state-of-the-art single object methods, especially for scenes with severely occluded objects. All code, training data, and scene statistics will be available for research use.

Connected through hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image"):
  with members:
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "learning to infer and execute 3d shape programs"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "few shot generalization for single image 3d reconstruction via priors"
    - "deep level sets implicit surface representations for 3d shape inference"

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)