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 "pyramid multi view stereo net with self adaptive view aggregation"):
  Abstract: In this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net for accurate and complete dense point cloud reconstruction. Different from existing deep-learning based MVS methods, our VA-MVSNet incorporates the cost variance between different views by introducing two novel self-adaptive view aggregation: pixel-wise view aggregation and voxel-wise view aggregation. Moreover, to enhance the point cloud reconstruction on the texture-less regions, we extend VA-MVSNet with pyramid multi-scale images input as PVA-MVSNet, where multi-metric constraints are leveraged to aggregate the reliable depth estimation at the coarser scale to fill-in the mismatched regions at the finer scale. Experimental results show that our approach establishes a new state-of-the-art on the DTU dataset with significant improvements in the completeness and overall quality of 3D reconstruction, and ranks 1st on the Tanks and Temples benchmark among all published deep-learning based methods. Our codebase is available at this https URL.

Includes members:
  Member "dpsnet end to end deep plane sweep stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "point based multi view stereo network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "recurrent mvsnet for high resolution multi view stereo depth inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  Member "multi scale geometric consistency guided multi view stereo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  Member "segstereo exploiting semantic information for disparity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "deepmvs learning multi view stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "edgestereo a context integrated residual pyramid network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "mvsnet depth inference for unstructured multi view stereo":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")

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)