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 "blendedmvs a large scale dataset for generalized multi view stereo networks"):
  Abstract: While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. To create the dataset, we apply a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, we render these mesh models to color images and depth maps. The rendered color images are further blended with the input images to generate photo-realistic blended images as the training input. Our dataset contains over 17k high-resolution images covering a variety of scenes, including cities, architectures, sculptures and small objects. Extensive experiments demonstrate that BlendedMVS endows the trained model with significantly better generalization ability compared with other MVS datasets. The entire dataset with pretrained models will be made publicly available at this https URL.

Includes members:
  Member "the replica dataset a digital replica of indoor spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an exploration of embodied visual exploration")
  Member "raynet learning volumetric 3d reconstruction with ray potentials":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "point based multi view stereo network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  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 "megadepth learning single view depth prediction from internet photos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "training deep networks with synthetic data bridging the reality gap by domain randomization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing generality in machine learning through procedural content generation")
  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)