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 "choicenet cnn learning through choice of multiple feature map representations"):
  Abstract: We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance, and encourages feature reuse. We evaluate our proposed architecture on three benchmark datasetsforobjectrecognitiontasks(CIFAR-10,CIFAR100, SVHN) and on a semantic segmentation dataset (CamVid).

Includes members:
  Member "adaptive affinity fields for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "efficient dense modules of asymmetric convolution for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "recurrent scene parsing with perspective understanding in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic flow for fast and accurate scene parsing")
  Member "understanding convolution for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "residual conv deconv grid network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "resnet in resnet generalizing residual architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional networks with dense connectivity")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

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)