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: "panorama to panorama matching for location recognition"
  Abstract: Location recognition is commonly treated as visual instance retrieval on "street view" imagery. The dataset items and queries are panoramic views, i.e. groups of images taken at a single location. This work introduces a novel panorama-to-panorama matching process, either by aggregating features of individual images in a group or by explicitly constructing a larger panorama. In either case, multiple views are used as queries. We reach near perfect location recognition on a standard benchmark with only four query views.

Connected through hyperedge HE-1 (induced by source paper "attention aware age agnostic visual place recognition"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "discriminative label consistent domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-2 (induced by source paper "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"):
  with members:
    - "openmpr recognize places using multimodal data for people with visual impairments"
    - "openseqslam2 0 an open source toolbox for visual place recognition under changing conditions"
    - "visual localization of key positions for visually impaired people"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "on the performance of convnet features for place recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "towards accurate camera geopositioning by image matching"):
  with members:
    - "large scale image retrieval with attentive deep local features"
    - "visual place recognition using landmark distribution descriptors"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "wide area image geolocalization with aerial reference imagery"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "very deep convolutional networks for large scale image recognition"

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)