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 "learning long range spatial dependencies with horizontal gated recurrent units"):
  Abstract: Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. Here, however, we show that these neural networks and their recent extensions struggle in recognition tasks where co-dependent visual features must be detected over long spatial ranges. We introduce the horizontal gated-recurrent unit (hGRU) to learn intrinsic horizontal connections -- both within and across feature columns. We demonstrate that a single hGRU layer matches or outperforms all tested feedforward hierarchical baselines including state-of-the-art architectures which have orders of magnitude more free parameters. We further discuss the biological plausibility of the hGRU in comparison to anatomical data from the visual cortex as well as human behavioral data on a classic contour detection task.

Includes members:
  Member "dilated deep residual network for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "do deep neural networks suffer from crowding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crowding in humans is unlike that in convolutional neural networks")
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arma nets expanding receptive field for dense prediction")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep predictive coding networks for video prediction and unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "bridging the gaps between residual learning recurrent neural networks and visual cortex":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional networks with dense connectivity")

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)