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 "seeing in the dark with recurrent convolutional neural networks"):
  Abstract: Classical convolutional neural networks (cCNNs) are very good at categorizing objects in images. But, unlike human vision which is relatively robust to noise in images, the performance of cCNNs declines quickly as image quality worsens. Here we propose to use recurrent connections within the convolutional layers to make networks robust against pixel noise such as could arise from imaging at low light levels, and thereby significantly increase their performance when tested with simulated noisy video sequences. We show that cCNNs classify images with high signal to noise ratios (SNRs) well, but are easily outperformed when tested with low SNR images (high noise levels) by convolutional neural networks that have recurrency added to convolutional layers, henceforth referred to as gruCNNs. Addition of Bayes-optimal temporal integration to allow the cCNN to integrate multiple image frames still does not match gruCNN performance. Additionally, we show that at low SNRs, the probabilities predicted by the gruCNN (after calibration) have higher confidence than those predicted by the cCNN. We propose to consider recurrent connections in the early stages of neural networks as a solution to computer vision under imperfect lighting conditions and noisy environments; challenges faced during real-time video streams of autonomous driving at night, during rain or snow, and other non-ideal situations.

Includes members:
  Member "adversarial examples that fool both computer vision and time limited humans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network")
  Member "learning to see in the dark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "comparing deep neural networks against humans object recognition when the signal gets weaker":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "unsupervised learning for physical interaction through video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "convolutional lstm network a machine learning approach for precipitation nowcasting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)