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: "addressing challenging place recognition tasks using generative adversarial networks"
  Abstract: Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.

Connected through hyperedge HE-1 (induced by source paper "scalable place recognition under appearance change for autonomous driving"):
  with members:
    - "semi parametric topological memory for navigation"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "polysemous codes"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-2 (induced by source paper "learning matchable colorspace transformations for long term metric visual localization"):
  with members:
    - "night to day image translation for retrieval based localization"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "dpc net deep pose correction for visual localization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-3 (induced by source paper "excavate condition invariant space by intrinsic encoder"):
  with members:
    - "binary generative adversarial networks for image retrieval"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep learning features at scale for visual place recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence for long term robot autonomy a survey"):
  with members:
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

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.RO(Robotics)