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: "reseg a recurrent neural network for object segmentation"
  Abstract: We propose a structured prediction architecture for images centered around deep recurrent neural networks. The proposed network, called ReSeg, is based on the recently introduced ReNet model for object classification. We modify and extend it to perform object segmentation, noting that the avoidance of pooling can greatly simplify pixel-wise tasks for images. The ReSeg layer is composed of four recurrent neural networks that sweep the image horizontally and vertically in both directions, along with a final layer that expands the prediction back to the original image size. ReSeg combines multiple ReSeg layers with several possible input layers as well as a final layer which expands the prediction back to the original image size, making it suitable for a variety of structured prediction tasks. We evaluate ReSeg on the specific task of object segmentation with three widely-used image segmentation datasets, namely Weizmann Horse, Fashionista and Oxford Flower. The results suggest that ReSeg can challenge the state of the art in object segmentation, and may have further applications in structured prediction at large.

Connected through hyperedge HE-1 (induced by source paper "waterfall atrous spatial pooling architecture for efficient semantic segmentation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "spatial temporal recurrent neural network for emotion recognition"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "semantic segmentation via highly fused convolutional network with multiple soft cost functions"):
  with members:
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic understanding of scenes through the ade20k dataset"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "video propagation networks"
    - "pyramid scene parsing network"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "dense transformer networks"):
  with members:
    - "deformable convolutional networks"
    - "stacked hourglass networks for human pose estimation"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "deeper depth prediction with fully convolutional residual networks"
    - "image to image translation with conditional adversarial networks"
    - "group equivariant convolutional networks"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"

Connected through hyperedge HE-5 (induced by source paper "deep contextual recurrent residual networks for scene labeling"):
  with members:
    - "identity mappings in deep residual networks"
    - "multi level contextual rnns with attention model for scene labeling"
    - "deep residual learning for image recognition"
    - "recurrent models of visual attention"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"
    - "fully convolutional networks for semantic segmentation"
    - "recurrent convolutional neural networks for scene parsing"

Connected through hyperedge HE-6 (induced by source paper "video scene parsing with predictive feature learning"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "approximate policy iteration for budgeted semantic video segmentation"
    - "optical flow with semantic segmentation and localized layers"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "joint optical flow and temporally consistent semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "representation of linguistic form and function in recurrent neural networks"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "visualizing and understanding neural models in nlp"
    - "understanding neural networks through deep visualization"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "recurrent fully convolutional networks for video segmentation"):
  with members:
    - "delving deeper into convolutional networks for learning video representations"
    - "conditional random fields as recurrent neural networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "densebox unifying landmark localization with end to end object detection"
    - "learning deconvolution network for semantic segmentation"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "going deeper with convolutions"

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)