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: "robust inference for visual inertial sensor fusion"
  Abstract: Inference of three-dimensional motion from the fusion of inertial and visual sensory data has to contend with the preponderance of outliers in the latter. Robust filtering deals with the joint inference and classification task of selecting which data fits the model, and estimating its state. We derive the optimal discriminant and propose several approximations, some used in the literature, others new. We compare them analytically, by pointing to the assumptions underlying their approximations, and empirically. We show that the best performing method improves the performance of state-of-the-art visual-inertial sensor fusion systems, while retaining the same computational complexity.

Connected through hyperedge HE-1 (induced by source paper "visual inertial odometry of aerial robots"):
  with members:
    - "visual inertial navigation a concise review"
    - "online temporal calibration for monocular visual inertial systems"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (induced by source paper "geo supervised visual depth prediction"):
  with members:
    - "visual inertial object detection and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-3 (induced by source paper "visual inertial object detection and mapping"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "mask r cnn"
    - "visual inertial semantic scene representation for 3 d object detection"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a survey of structure from motion"):
  with members:
    - "a survey on non filter based monocular visual slam systems"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "shapefit exact location recovery from corrupted pairwise directions"
    - "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "eigenvector synchronization graph rigidity and the molecule problem"

Connected through hyperedge HE-5 (induced by source paper "probe gk predictive robust estimation using generalized kernels"):
  with members:
    - "probe predictive robust estimation for visual inertial navigation"

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)