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: "learning based image enhancement for visual odometry in challenging hdr environments"
  Abstract: One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a Deep Neural Network (DNN) with the purpose of obtaining enhanced representations of the sequences for VO. Then, we demonstrate how the insertion of Long Short Term Memory (LSTM) allows us to obtain temporally consistent sequences, as the estimation depends on previous states. However, the use of very deep networks does not allow the insertion into a real-time VO framework; therefore, we also propose a Convolutional Neural Network (CNN) of reduced size capable of performing faster. Finally, we validate the enhanced representations by evaluating the sequences produced by the two architectures in several state-of-art VO algorithms, such as ORB-SLAM and DSO.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "gn net the gauss newton loss for multi weather relocalization"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "open3d a modern library for 3d data processing"
    - "neighbourhood consensus networks"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "taking a deeper look at the inverse compositional algorithm"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "superpoint self supervised interest point detection and description"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-3 (induced by source paper "multi frame gan image enhancement for stereo visual odometry in low light"):
  with members:
    - "video to video synthesis"
    - "reconet real time coherent video style transfer network"
    - "learning linear transformations for fast arbitrary style transfer"
    - "night to day image translation for retrieval based localization"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "drit diverse image to image translation via disentangled representations"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "deeppco end to end point cloud odometry through deep parallel neural network"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "selective sensor fusion for neural visual inertial odometry"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-6 (induced by source paper "ground plane based absolute scale estimation for monocular visual odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "probabilistic global scale estimation for monoslam based on generic object detection"
    - "a survey of structure from motion"
    - "inertial based scale estimation for structure from motion on mobile devices"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "relative camera pose estimation using convolutional neural networks"
    - "reducing drift in visual odometry by inferring sun direction using a bayesian convolutional neural network"
    - "light source estimation with analytical path tracing"
    - "find your way by observing the sun and other semantic cues"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "direct sparse odometry"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "ls vo learning dense optical subspace for robust visual odometry estimation"):
  with members:
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "towards visual ego motion learning in robots"
    - "learning rank reduced interpolation with principal component analysis"
    - "generative visual manipulation on the natural image manifold"

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)