human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "single image 3d interpreter network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "physically based rendering for indoor scene understanding using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "opengm a c library for discrete graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood")

Question: Please tell me which class the source paper 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)