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: "learning contextual dependence with convolutional hierarchical recurrent neural networks"
  Abstract: Deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependence among different image regions. However, such dependence is very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information in sequential data, and they only require a limited number of network parameters. Thus, we proposed the hierarchical RNNs (HRNNs) to encode the contextual dependence in image representation. In HRNNs, each RNN layer focuses on modeling spatial dependence among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two RNN models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost and 2) hierarchical long-short term memory recurrent network, which performs better than HSRN with the price of higher computational cost. In this paper, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical RNNs (C-HRNNs) for image classification. C-HRNNs not only utilize the discriminative representation power of CNNs, but also utilize the contextual dependence learning ability of our HRNNs. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve the state-of-the-art results on Places 205, SUN 397, and MIT indoor, and the competitive results on ILSVRC 2012.

Connected through hyperedge HE-1 (induced by source paper "scene recognition with prototype agnostic scene layout"):
  with members:
    - "semantic fisher scores for task transfer using objects to classify scenes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "graph r cnn for scene graph generation"
    - "a deeper look at power normalizations"
    - "situation recognition with graph neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "vehicle re identification exploring feature fusion using multi stream convolutional networks"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "you only look once unified real time object detection"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "dense recurrent neural networks for scene labeling"):
  with members:
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "hierarchical question image co attention for visual question answering"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "scene parsing via dense recurrent neural networks with attentional selection"):
  with members:
    - "context encoding for semantic segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "sanet structure aware network for visual tracking"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"

Connected through hyperedge HE-5 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "real time action recognition with enhanced motion vector cnns"
    - "places an image database for deep scene understanding"
    - "weakly supervised fine grained categorization with part based image representation"
    - "improving multi label learning with missing labels by structured semantic correlations"

Connected through hyperedge HE-6 (induced by source paper "locally supervised deep hybrid model for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "text attentional convolutional neural network for scene text detection"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "detecting text in natural image with connectionist text proposal network"
    - "local color contrastive descriptor for image classification"
    - "reading scene text in deep convolutional sequences"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised patchnets describing and aggregating local patches for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "object scene convolutional neural networks for event recognition in images"
    - "local color contrastive descriptor for image classification"

Connected through hyperedge HE-8 (induced by source paper "multi level contextual rnns with attention model for scene labeling"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "sanet structure aware network for visual tracking"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pixel recurrent neural networks"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "conditional random fields as recurrent neural networks"

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)