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: "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.

Connected through hyperedge HE-1 (induced by source paper "adaptive class weight based dual focal loss for improved semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "total recall understanding traffic signs using deep hierarchical convolutional neural networks"):
  with members:
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "parsenet looking wider to see better"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "vehicle local position estimation system"
    - "a novel plsa based traffic signs classification system"

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)