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 "espnetv2 a light weight power efficient and general purpose convolutional neural network"):
  Abstract: We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (1) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at this https URL

Includes members:
  Member "igcv3 interleaved low rank group convolutions for efficient deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "xsepconv extremely separated convolution")
  Member "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fbnet hardware aware efficient convnet design via differentiable neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  Member "rtseg real time semantic segmentation comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trained trajectory based automated parking system using visual slam")
  Member "contextnet exploring context and detail for semantic segmentation in real time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution")
  Member "alternating multi bit quantization for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantized neural network inference with precision batching")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "post training piecewise linear quantization for deep neural networks")

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)