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: "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
  Abstract: State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%, 2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20 object classes.

Connected through hyperedge HE-1 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "aanet attribute attention network for person re identifications"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "beyond forward shortcuts fully convolutional master slave networks msnets with backward skip connections for semantic segmentation"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "semantic understanding of scenes through the ade20k dataset"
    - "bridging category level and instance level semantic image segmentation"
    - "deep networks with stochastic depth"
    - "the importance of skip connections in biomedical image segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "cavs a vertex centric programming interface for dynamic neural networks"):
  with members:
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "deep learning with dynamic computation graphs"
    - "on the fly operation batching in dynamic computation graphs"
    - "semantic object parsing with graph lstm"
    - "efficient softmax approximation for gpus"
    - "tensorflow a system for large scale machine learning"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-4 (induced by source paper "partial labeled gastric tumor segmentation via patch based reiterative learning"):
  with members:
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "beyond forward shortcuts fully convolutional master slave networks msnets with backward skip connections for semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "dcan deep contour aware networks for accurate gland segmentation"

Connected through hyperedge HE-5 (induced by source paper "scene labeling using gated recurrent units with explicit long range conditioning"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "multi level contextual rnns with attention model for scene labeling"
    - "learning common and specific features for rgb d semantic segmentation with deconvolutional networks"
    - "multi path feedback recurrent neural network for scene parsing"
    - "pixel recurrent neural networks"
    - "semantic image segmentation via deep parsing network"

Connected through hyperedge HE-6 (induced by source paper "a deep spatial contextual long term recurrent convolutional network for saliency detection"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "video salient object detection via fully convolutional networks"
    - "a deep multi level network for saliency prediction"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "visual saliency detection based on multiscale deep cnn features"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "semantic segmentation of earth observation data using multimodal and multi scale deep networks"):
  with members:
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "competitive multi scale convolution"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)