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: "geo distinctive visual element matching for location estimation of images"
  Abstract: We propose an image representation and matching approach that substantially improves visual-based location estimation for images. The main novelty of the approach, called distinctive visual element matching (DVEM), is its use of representations that are specific to the query image whose location is being predicted. These representations are based on visual element clouds, which robustly capture the connection between the query and visual evidence from candidate locations. We then maximize the influence of visual elements that are geo-distinctive because they do not occur in images taken at many other locations. We carry out experiments and analysis for both geo-constrained and geo-unconstrained location estimation cases using two large-scale, publicly-available datasets: the San Francisco Landmark dataset with $1.06$ million street-view images and the MediaEval '15 Placing Task dataset with $5.6$ million geo-tagged images from Flickr. We present examples that illustrate the highly-transparent mechanics of the approach, which are based on common sense observations about the visual patterns in image collections. Our results show that the proposed method delivers a considerable performance improvement compared to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "geo aware networks for fine grained recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil is in the tails fine grained classification in the wild"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cvae gan fine grained image generation through asymmetric training"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-2 (induced by source paper "relative geometry aware siamese neural network for 6dof camera relocalization"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "image based localization using hourglass networks"
    - "geometry aware learning of maps for camera localization"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "webvision challenge visual learning and understanding with web data"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "adaptive deep pyramid matching for remote sensing scene classification"

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.MM(Multimedia)