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: "keyframe based direct thermal inertial odometry"
  Abstract: This paper proposes an approach for fusing direct radiometric data from a thermal camera with inertial measurements to extend the robotic capabilities of aerial robots for navigation in GPS–denied and visually degraded environments in the conditions of darkness and in the presence of airborne obscurants such as dust, fog and smoke. An optimization based approach is developed that jointly minimizes the re–projection error of 3D landmarks and inertial measurement errors. The developed solution is extensively verified against both ground– truth in an indoor laboratory setting, as well as inside an underground mine under severely visually degraded conditions.

Connected through hyperedge HE-1 (induced by source paper "pyramidal edge maps based guided thermal super resolution"):
  with members:
    - "multimodal sensor fusion in single thermal image super resolution"
    - "multimedia super resolution via deep learning a survey"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "richer convolutional features for edge detection"
    - "fully convolutional siamese networks for object tracking"
    - "densely connected convolutional networks"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination"):
  with members:
    - "selective sensor fusion for neural visual 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"

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)