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: "high precision localization using ground texture"
  Abstract: Location-aware applications play an increasingly critical role in everyday life. However, the most common global localization technology - GPS - has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local features in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of carpet fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces.

Connected through hyperedge HE-1 (induced by source paper "features for ground texture based localization a survey"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "latch learned arrangements of three patch codes"

Connected through hyperedge HE-2 (induced by source paper "ground texture based localization using compact binary descriptors"):
  with members:
    - "features for ground texture based localization a survey"
    - "latch learned arrangements of three patch codes"

Connected through hyperedge HE-3 (induced by source paper "tree bark re identification using a deep learning feature descriptor"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "lift learned invariant feature transform"
    - "sift meets cnn a decade survey of instance retrieval"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "automatic coverage selection for surface based visual localization"):
  with members:
    - "particular object retrieval with integral max pooling of cnn activations"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

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)