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: "large scale image geo localization using dominant sets"
  Abstract: This paper presents a new approach for the challenging problem of geo-locating an image using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem on local image features. Akin to existing approaches on the problem, our framework builds on low-level features which allow partial matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster which we use to dynamically select the number of nearest neighbors (typically coming from multiple reference images) for each query feature based on its discrimination value. Second, as we also quantify in our experiments, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluated the proposed framework on an existing dataset of 102k street view images as well as a new dataset of 300k images, and show that it outperforms the state-of-the-art by 20% and 7%, respectively, on the two datasets.

Connected through hyperedge HE-1 (induced by source paper "egoreid dataset person re identification in videos acquired by mobile devices with first person point of view"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "vlase vehicle localization by aggregating semantic edges"):
  with members:
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "cross view image matching for geo localization in urban environments"
    - "semantic visual localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "casenet deep category aware semantic edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "localizing and orienting street views using overhead imagery"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "a graph transduction game for multi target tracking"):
  with members:
    - "applications of a graph theoretic based clustering framework in computer vision and pattern recognition"
    - "dominant sets for constrained image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a continuous time quantum walk for attributed graphs matching"):
  with members:
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"

Connected through hyperedge HE-5 (induced by source paper "multi target tracking in multiple non overlapping cameras using constrained dominant sets"):
  with members:
    - "performance measures and a data set for multi target multi camera tracking"
    - "interactive image segmentation using constrained dominant sets"
    - "deep residual learning for image recognition"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "alid scalable dominant cluster detection"

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)