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: "single view place recognition under seasonal changes"
  Abstract: Single-view place recognition, that we can define as finding an image that corresponds to the same place as a given query image, is a key capability for autonomous navigation and mapping. Although there has been a considerable amount of research in the topic, the high degree of image variability (with viewpoint, illumination or occlusions for example) makes it a research challenge. #R##N#One of the particular challenges, that we address in this work, is weather variation. Seasonal changes can produce drastic appearance changes, that classic low-level features do not model properly. Our contributions in this paper are twofold. First we pre-process and propose a partition for the Nordland dataset, frequently used for place recognition research without consensus on the partitions. And second, we evaluate several neural network architectures such as pre-trained, siamese and triplet for this problem. Our best results outperform the state of the art of the field. A video showing our results can be found in this https URL. The partitioned version of the Nordland dataset at this http URL.

Connected through hyperedge HE-1 (induced by source paper "condition invariant multi view place recognition"):
  with members:
    - "loosely coupled semi direct monocular slam"
    - "deep learning features at scale for visual place recognition"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "deep residual learning for image recognition"
    - "training a convolutional neural network for appearance invariant place recognition"
    - "on the performance of convnet features for place recognition"

Connected through hyperedge HE-2 (induced by source paper "empty cities image inpainting for a dynamic object invariant space"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "dynamic objects segmentation for visual localization in urban environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "generative image inpainting with contextual attention"
    - "image inpainting using multi scale feature image translation"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "deep image prior"
    - "carla an open urban driving simulator"

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)