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 "learning to infer implicit surfaces without 3d supervision"):
  Abstract: Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images, which is invaluable for training. The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output. While explicit representations, such as point clouds and voxels, can span a wide range of shape variations, their resolutions are often limited. Mesh-based representations are more efficient but are limited by their ability to handle varying topologies. Implicit surfaces, however, can robustly handle complex shapes, topologies, and also provide flexible resolution control. We address the fundamental problem of learning implicit surfaces for shape inference without the need of 3D supervision. Despite their advantages, it remains nontrivial to (1) formulate a differentiable connection between implicit surfaces and their 2D renderings, which is needed for image-based supervision; and (2) ensure precise geometric properties and control, such as local smoothness. In particular, sampling implicit surfaces densely is also known to be a computationally demanding and very slow operation. To this end, we propose a novel ray-based field probing technique for efficient image-to-field supervision, as well as a general geometric regularizer for implicit surfaces, which provides natural shape priors in unconstrained regions. We demonstrate the effectiveness of our framework on the task of single-view image-based 3D shape digitization and show how we outperform state-of-the-art techniques both quantitatively and qualitatively.

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 "inverse path tracing for joint material and lighting estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seeing the world in a bag of chips")
  Member "soft rasterizer a differentiable renderer for image based 3d reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "deep level sets implicit surface representations for 3d shape inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "learning implicit fields for generative shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pixel2mesh generating 3d mesh models from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "atlasnet a papier m ach e approach to learning 3d surface generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "unsupervised training for 3d morphable model regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")

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)