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: "selective sensor fusion for neural visual inertial odometry"
  Abstract: Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data. We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization. In particular, we propose two fusion modalities based on different masking strategies: deterministic soft fusion and stochastic hard fusion, and we compare with previously proposed direct fusion baselines. During testing, the network is able to selectively process the features of the available sensor modalities and produce a trajectory at scale. We present a thorough investigation on the performances on three public autonomous driving, Micro Aerial Vehicle (MAV) and hand-held VIO datasets. The results demonstrate the effectiveness of the fusion strategies, which offer better performances compared to direct fusion, particularly in presence of corrupted data. In addition, we study the interpretability of the fusion networks by visualising the masking layers in different scenarios and with varying data corruption, revealing interesting correlations between the fusion networks and imperfect sensory input data.

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:
    - "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"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-2 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization"):
  with members:
    - "fuseseg lidar point cloud segmentation fusing multi modal data"
    - "sensor fusion using backward shortcut connections for sleep apnea detection in multi modal data"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "atloc attention guided camera localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "geometric loss functions for camera pose regression with deep learning"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "selfvio self supervised deep monocular visual inertial odometry and depth estimation"):
  with members:
    - "deeptio a deep thermal inertial odometry with visual hallucination"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"

Connected through hyperedge HE-4 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"
    - "global pose estimation with an attention based recurrent network"

Connected through hyperedge HE-5 (induced by source paper "atloc attention guided camera localization"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "context aware self attention networks"
    - "image transformer"
    - "deep auxiliary learning for visual localization and odometry"
    - "global pose estimation with an attention based recurrent network"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

Connected through hyperedge HE-6 (induced by source paper "deep direct visual odometry"):
  with members:
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "dsvo direct stereo visual odometry"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-7 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination"):
  with members:
    - "keyframe based direct thermal inertial odometry"
    - "distilling knowledge from a deep pose regressor network"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-8 (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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "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"

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)