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 "mvs 2 deep unsupervised multi view stereo with multi view symmetry"):
  Abstract: The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In this paper, we propose the first unsupervised learning based MVS network, which learns the multi-view depth maps from the input multi-view images and does not need ground-truth 3D training data. Our network is symmetric in predicting depth maps for all views simultaneously, where we enforce cross-view consistency of multi-view depth maps during both training and testing stages. Thus, the learned multi-view depth maps naturally comply with the underlying 3D scene geometry. Besides, our network also learns the multi-view occlusion maps, which further improves the robustness of our network in handling real-world occlusions. Experimental results on multiple benchmarking datasets demonstrate the effectiveness of our network and the excellent generalization ability.

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 "recurrent mvsnet for high resolution multi view stereo depth inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "deepmvs learning multi view stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "open world stereo video matching with deep rnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group wise correlation stereo network")
  Member "mvsnet depth inference for unstructured multi view stereo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "mvdepthnet real time multiview depth estimation neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")

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)