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 "efficient yet deep convolutional neural networks for semantic segmentation"):
  Abstract: Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer strides and utilizes deconvolutional layers for upsampling, it has been a go to for any image segmentation task. We propose two segmentation architecture transferring weights from the popular classification neural net VGG19 and VGG16 which were trained on Imagenet classification dataset, transform all the fully connected layers to convolutional layers, use dilated convolution for decreasing the parameters, moreover we add more finer strides and attach four skip architectures which are concatenated with the deconvolutional layers in steps. We train on two stages, first with PASCAL VOC2012 training data and then with SBD training and validation set. With our model, FCN-2s-Dilated-VGG19 we yield better score for PASCAL VOC2012 test set with a meanIOU of 69 percent which is 1.8 percent better than FCN-8s. And with FCN-2s-Dilated-VGG16 we score a meanIOU of 67.6 percent. On the other hand our model consumes up to 10-20 percent less memory than FCN-8s for training with NVIDIA Pascal GPUs, making it more efficient and less memory consuming architecture for pixel-wise segmentation.

Includes members:
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "weakly and semi supervised learning of a dcnn for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification")
  Member "instance aware semantic segmentation via multi task network cascades":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "parsenet looking wider to see better":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "attention to scale scale aware semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "multi scale context aggregation by dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "learning deconvolution network for semantic segmentation":
    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)