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: "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"
  Abstract: Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications. However, direct methods remain brittle in the face of appearance change due to their underlying assumption of photometric consistency, which is commonly violated in practice. In this paper, we propose to mitigate this problem by training deep convolutional encoder-decoder models to transform images of a scene such that they correspond to a previously-seen canonical appearance. We validate our method in multiple environments and illumination conditions using high-fidelity synthetic RGB-D datasets, and integrate the trained models into a direct visual localization pipeline, yielding improvements in visual odometry (VO) accuracy through time-varying illumination conditions, as well as improved metric relocalization performance under illumination change, where conventional methods normally fail. We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. An open-source implementation of our method using PyTorch is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "geometric consistency for self supervised end to end visual odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "improved visual relocalization by discovering anchor points"

Connected through hyperedge HE-2 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "map management for efficient long term visual localization in outdoor environments"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "addressing challenging place recognition tasks using generative 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-4 (induced by source paper "fast rigid 3d registration solution a simple method free of svd and eigen decomposition"):
  with members:
    - "fast symbolic 3 d registration solution"
    - "optimized data representation for interactive multiview navigation"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-5 (induced by source paper "spp net deep absolute pose regression with synthetic views"):
  with members:
    - "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"
    - "planet photo geolocation with convolutional neural networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "inverting visual representations with convolutional networks"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-6 (induced by source paper "dpc net deep pose correction for visual localization"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "topometric localization with deep learning"
    - "probe gk predictive robust estimation using generalized kernels"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "benchmarking deep reinforcement learning for continuous control"
    - "backprop kf learning discriminative deterministic state estimators"

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)