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: "anisotropic scale junction detection and matching for indoor images"
  Abstract: Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales, which, however, contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an  a-contrario  model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction and are thus named as  anisotropic-scale junctions  (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g., key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-2 (induced by source paper "holistically attracted wireframe parsing"):
  with members:
    - "learning regional attraction for line segment detection"
    - "learning to calibrate straight lines for fisheye image rectification"
    - "ppgnet learning point pair graph for line segment detection"
    - "surface reconstruction from 3d line segments"
    - "point based multi view stereo network"
    - "end to end wireframe parsing"
    - "plmp point line minimal problems in complete multi view visibility"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-3 (induced by source paper "learning regional attraction for line segment detection"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "learning attraction field representation for robust line segment detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "convolutional oriented boundaries"

Connected through hyperedge HE-4 (induced by source paper "mini uav based remote sensing techniques applications and prospectives"):
  with members:
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "hierarchical motion consistency constraint for efficient geometrical verification in uav image matching"
    - "image stitching by line guided local warping with global similarity constraint"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"

Connected through hyperedge HE-5 (induced by source paper "learning attraction field representation for robust line segment detection"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "image stitching by line guided local warping with global similarity constraint"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "convolutional oriented boundaries"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-6 (induced by source paper "adaptively transforming graph matching"):
  with members:
    - "alternating direction graph matching"
    - "optimal transport for domain adaptation"
    - "a path following algorithm for the graph matching problem"
    - "learning graph matching"

Connected through hyperedge HE-7 (induced by source paper "geosay a geometric saliency for extracting buildings in remote sensing images"):
  with members:
    - "accurate building detection in vhr remote sensing images using geometric saliency"

Connected through hyperedge HE-8 (induced by source paper "image stitching by line guided local warping with global similarity constraint"):
  with members:
    - "image stitching with perspective preserving warping"

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)