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 "3d gmnet learning to estimate 3d shape from a single image as a gaussian mixture"):
  Abstract: In this paper, we introduce 3D-GMNet, a deep neural network for single-image 3D shape recovery. As the name suggests, 3D-GMNet recovers 3D shape as a Gaussian mixture model. In contrast to voxels, point clouds, or meshes, a Gaussian mixture representation requires a much smaller footprint for representing 3D shapes and, at the same time, offers a number of additional advantages including instant pose estimation, automatic level-of-detail computation, and a distance measure. The proposed 3D-GMNet is trained end-to-end with single input images and corresponding 3D models by using two novel loss functions: a 3D Gaussian mixture loss and a multi-view 2D loss. The first maximizes the likelihood of the Gaussian mixture shape representation by considering the target point cloud as samples from the true distribution, and the latter improves the consistency between the input silhouette and the projection of the Gaussian mixture shape model. Extensive quantitative evaluations with synthesized and real images demonstrate the effectiveness of the proposed method.

Includes members:
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "superquadrics revisited learning 3d shape parsing beyond cuboids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "im2struct recovering 3d shape structure from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "pixel2mesh generating 3d mesh models from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pix3d dataset and methods for single image 3d shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "occupancy networks learning 3d reconstruction in function space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "learning efficient point cloud generation for dense 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential forecasting of 100 000 points")
  Member "neural 3d mesh renderer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")

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)