human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  Abstract: Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training -- severely limiting their usability by the broader neuroscience community. To fill this gap, we describe a novel connectomics challenge for source- and tissue-agnostic reconstruction of neurons (STAR), which favors broad generalization over fitting specific datasets. We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge. We further describe a novel convolutional recurrent neural network module that combines short-range horizontal connections within a processing stage and long-range top-down connections between stages. The resulting architecture establishes the state of the art on the STAR challenge and represents a significant step towards widely usable and fully-automated connectomics analysis.

Includes members:
  Member "bi directional cascade network for perceptual edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "deep predictive coding network for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differential dynamic programming neural optimizer")
  Member "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel recurrent encoder decoder structure for large scale multi view stereo reconstruction from an open aerial dataset")
  Member "deep predictive coding network with local recurrent processing for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prednet and predictive coding a critical review")
  Member "learning long range spatial dependencies with horizontal gated recurrent units":
    also connected through other hyperedges:
      (none)
  Member "do cifar 10 classifiers generalize to cifar 10":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "can recurrent neural networks warp time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving the gating mechanism of recurrent neural networks")
  Member "learning what and where to attend":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic region pooling make attention more expressive")

Question: Please tell me which class the source paper 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)