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: "sample efficient image segmentation through recurrence"
  Abstract: There is a growing consensus in vision science that recurrent neural networks constitute better models of visual cortex than feedforward architectures. Yet, feedforward neural networks continue to dominate most popular computer vision challenges. We bridge this gap with the Gamma-net. Inspired by recurrent feedback loops prevalent in the mammalian visual cortex, Gamma-net introduces gated recurrent dynamics through feedforward, horizontal, and top-down connections into the popular U-Net architecture. We demonstrate that Gamma-net performs on par or better than state-of-the-art architectures for dense prediction in both natural image and cell segmentation datasets. The re-entrant processing of the Gamma-net lead to especially large performance gains over the state-of-the-art on smaller datasets. We further show that Gamma-net reproduces a contextual bias in orientation estimation which is consistent with the tilt illusion in human psychophysics. The existence of this bias in Gamma-net -- which emerges from contour detection training in natural images -- supports the theory that this visual illusion is a byproduct of recurrent computational mechanisms underlying contour detection. Vision science theory suggests that recurrent processing underlies robust biological vision, and we demonstrate that similar principles can improve the data efficiency of computer vision systems.

Connected through hyperedge HE-1 (induced by source paper "disentangling neural mechanisms for perceptual grouping"):
  with members:
    - "deep predictive coding network for object recognition"
    - "one shot segmentation in clutter"
    - "can recurrent neural networks warp time"
    - "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "dynamic routing between capsules"
    - "understanding deep learning requires rethinking generalization"
    - "deep predictive coding networks for video prediction and unsupervised learning"

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)