human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an end to end approach for speeding up neural network inference"
  Abstract: Important applications such as mobile computing require reducing the computational costs of neural network inference. Ideally, applications would specify their preferred tradeoff between accuracy and speed, and the network would optimize this end-to-end, using classification error to remove parts of the network \cite{lecun1990optimal,mozer1989skeletonization,BMVC2016_104}. Increasing speed can be done either during training -- e.g., pruning filters \cite{li2016pruning} -- or during inference -- e.g., conditionally executing a subset of the layers \cite{aig}. We propose a single end-to-end framework that can improve inference efficiency in both settings. We introduce a batch activation loss and use Gumbel reparameterization to learn network structure \cite{aig,jang2016categorical}. We train end-to-end against batch activation loss combined with classification loss, and the same technique supports pruning as well as conditional computation. We obtain promising experimental results for ImageNet classification with ResNet \cite{he2016resnet} (45-52\% less computation) and MobileNetV2 \cite{sandler2018mobilenetv2} (19-37\% less computation).

Connected through hyperedge HE-1 (induced by source paper "spatially adaptive inference with stochastic feature sampling and interpolation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "importance estimation for neural network pruning"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "sbnet sparse blocks network for fast inference"
    - "deformable convnets v2 more deformable better results"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Question: Please tell me which class the center node 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)