human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi scale geometric consistency guided multi view stereo"
  Abstract: In this paper, we propose an efficient multi-scale geometric consistency guided multi-view stereo method for accurate and complete depth map estimation. We first present our basic multi-view stereo method with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH). It leverages structured region information to sample better candidate hypotheses for propagation and infer the aggregation view subset at each pixel. For the depth estimation of low-textured areas, we further propose to combine ACMH with multi-scale geometric consistency guidance (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. To correct the erroneous estimates propagated from the coarser scales, we present a novel detail restorer. Experiments on extensive datasets show our method achieves state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details.

Connected through hyperedge HE-1 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-2 (induced by source paper "pyramid multi view stereo net with self adaptive view aggregation"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deepmvs learning multi view stereopsis"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "learning inverse depth regression for multi view stereo with correlation cost volume"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-4 (induced by source paper "deepc mvs deep confidence prediction for multi view stereo reconstruction"):
  with members:
    - "joint graph based depth refinement and normal estimation"
    - "learned collaborative stereo refinement"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "tapa mvs textureless aware patchmatch multi view stereo"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "the fast bilateral solver"

Connected through hyperedge HE-5 (induced by source paper "tsrnet scalable 3d surface reconstruction network for point clouds using tangent convolution"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "tangent convolutions for dense prediction in 3d"
    - "occupancy networks learning 3d reconstruction in function space"
    - "octnetfusion learning depth fusion from data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-6 (induced by source paper "joint graph based depth refinement and normal estimation"):
  with members:
    - "deepc mvs deep confidence prediction for multi view stereo reconstruction"
    - "learned collaborative stereo refinement"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "the fast bilateral solver"
    - "adam a method for stochastic optimization"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-7 (induced by source paper "planar prior assisted patchmatch multi view stereo"):
  with members:
    - "tapa mvs textureless aware patchmatch multi view stereo"

Question: Please tell me which class the center node 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)