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: "distinguishing refracted features using light field cameras with application to structure from motion"
  Abstract: Robots must reliably interact with refractive objects in many applications; however, refractive objects can cause many robotic vision algorithms to become unreliable or even fail, particularly feature-based matching applications, such as structure-from-motion. We propose a method to distinguish between refracted and Lambertian image features using a light field camera. Specifically, we propose to use textural cross-correlation to characterise apparent feature motion in a single light field, and compare this motion to its Lambertian equivalent based on 4D light field geometry. Our refracted feature distinguisher has a 34.3% higher rate of detection compared to state-of-the-art for light fields captured with large baselines relative to the refractive object. Our method also applies to light field cameras with much smaller baselines than previously considered, yielding up to 2 times better detection for 2D-refractive objects, such as a sphere, and up to 8 times better for 1D-refractive objects, such as a cylinder. For structure from motion, we demonstrate that rejecting refracted features using our distinguisher yields up to 42.4% lower reprojection error, and lower failure rate when the robot is approaching refractive objects. Our method lead to more robust robot vision in the presence of refractive objects.

Connected through hyperedge HE-1 (induced by source paper "lit light field inference of transparency for refractive object localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "glassloc plenoptic grasp pose detection in transparent clutter"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "glassloc plenoptic grasp pose detection in transparent clutter"):
  with members:
    - "plenoptic monte carlo object localization for robot grasping under layered translucency"
    - "sum sequential scene understanding and manipulation"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "high precision grasp pose detection in dense clutter"
    - "real time grasp detection using convolutional neural networks"
    - "deep learning for detecting robotic grasps"

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)