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: "semantic cross view matching"
  Abstract: Matching cross-view images is challenging because the appearance and viewpoints are significantly different. While low-level features based on gradient orientations or filter responses can drastically vary with such changes in viewpoint, semantic information of images however shows an invariant characteristic in this respect. Consequently, semantically labeled regions can be used for performing cross-view matching. In this paper, we therefore explore this idea and propose an automatic method for detecting and representing the semantic information of an RGB image with the goal of performing cross-view matching with a (non-RGB) geographic information system (GIS). A segmented image forms the input to our system with segments assigned to semantic concepts such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to robustly capture both, the presence of semantic concepts and the spatial layout of those segments. Pairwise distances between the descriptors extracted from the GIS map and the query image are then used to generate a shortlist of the most promising locations with similar semantic concepts in a consistent spatial layout. An experimental evaluation with challenging query images and a large urban area shows promising results.

Connected through hyperedge HE-1 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception"):
  with members:
    - "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"
    - "bridging the domain gap for ground to aerial image matching"
    - "lending orientation to neural networks for cross view geo localization"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "cross view image matching for geo localization in urban environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "wide area image geolocalization with aerial reference imagery"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "lending orientation to neural networks for cross view geo localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "cross view image synthesis using conditional gans"
    - "cross view image matching for geo localization in urban environments"
    - "deep complex networks"
    - "deep metric learning with angular loss"
    - "on complex valued convolutional neural networks"
    - "localizing and orienting street views using overhead imagery"

Connected through hyperedge HE-3 (induced by source paper "optimal feature transport for cross view image geo localization"):
  with members:
    - "lending orientation to neural networks for cross view geo localization"
    - "feature selection for unsupervised domain adaptation using optimal transport"
    - "deeppermnet visual permutation learning"
    - "localizing and orienting street views using overhead imagery"
    - "semantic image based geolocation given a map"
    - "predicting ground level scene layout from aerial imagery"
    - "optimal transport for domain adaptation"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "joint facade registration and segmentation for urban localization"):
  with members:
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "efficient 2d and 3d facade segmentation using auto context"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fully convolutional networks for semantic segmentation"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-5 (induced by source paper "semantic visual localization"):
  with members:
    - "learning compact geometric features"
    - "deep learning features at scale for visual place recognition"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "segmatch segment based place recognition in 3d point clouds"
    - "lift learned invariant feature transform"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "generic 3d representation via pose estimation and matching"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "find your way by observing the sun and other semantic cues"):
  with members:
    - "going deeper with convolutions"
    - "object detectors emerge in deep scene cnns"

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)