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 "convolutional networks with adaptive inference graphs"):
  Abstract: Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which layer to compute next. In this work, we propose convolutional networks with adaptive inference graphs (ConvNet-AIG) that adaptively define their network topology conditioned on the input image. Following a high-level structure similar to residual networks (ResNets), ConvNet-AIG decides for each input image on the fly which layers are needed. In experiments on ImageNet we show that ConvNet-AIG learns distinct inference graphs for different categories. Both ConvNet-AIG with 50 and 101 layers outperform their ResNet counterpart, while using 20% and 33% less computations respectively. By grouping parameters into layers for related classes and only executing relevant layers, ConvNet-AIG improves both efficiency and overall classification quality. Lastly, we also study the effect of adaptive inference graphs on the susceptibility towards adversarial examples. We observe that ConvNet-AIG shows a higher robustness than ResNets, complementing other known defense mechanisms.

Includes members:
  Member "outrageously large neural networks the sparsely gated mixture of experts layer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised question decomposition for question answering")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "demystifying neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "arbitrary style transfer in real time with adaptive instance normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "π nets deep polynomial neural networks")
  Member "branchynet fast inference via early exiting from deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accelerating object detection by erasing background activations")
  Member "multi scale dense networks for resource efficient image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)