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: "attribute graph a graph based approach to image ranking"
  Abstract: We propose a novel image representation, termed Attribute-Graph, to rank images by their semantic similarity to a given query image. An Attribute-Graph is an undirected fully connected graph, incorporating both local and global image characteristics. The graph nodes characterise objects as well as the overall scene context using mid-level semantic attributes, while the edges capture the object topology. We demonstrate the effectiveness of Attribute-Graphs by applying them to the problem of image ranking. We benchmark the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets, which we have created in order to evaluate the ranking performance on complex queries containing multiple objects. Our experimental evaluation shows that modelling images as Attribute-Graphs results in improved ranking performance over existing techniques.

Connected through hyperedge HE-1 (induced by source paper "union visual translation embedding for visual relationship detection and scene graph generation"):
  with members:
    - "neural baby talk"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-2 (induced by source paper "image classification with hierarchical multigraph networks"):
  with members:
    - "graph u nets"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "iterative visual reasoning beyond convolutions"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-3 (induced by source paper "image retrieval with mixed initiative and multimodal feedback"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "end to end learning of action detection from frame glimpses in videos"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "discovering attribute shades of meaning with the crowd"
    - "deep reinforcement learning with double q learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"):
  with members:
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "semantic segmentation using adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "improved deep learning of object category using pose information"
    - "network of experts for large scale image categorization"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-5 (induced by source paper "identifying most walkable direction for navigation in an outdoor environment"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "region graph based method for multi object detection and tracking using depth cameras"
    - "wide residual networks"
    - "pyramid scene parsing network"
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-6 (induced by source paper "region based image retrieval revisited"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "visual relationship detection with language priors"
    - "raid a relation augmented image descriptor"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "fast r cnn"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "very deep convolutional networks for large scale image recognition"
    - "neural codes for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "deep image representations using caption generators"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "densecap fully convolutional localization networks for dense captioning"
    - "object level deep feature pooling for compact image representation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-8 (induced by source paper "on support relations and semantic scene graphs"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "what happens if learning to predict the effect of forces in images"
    - "towards scene understanding with detailed 3d object representations"
    - "fast r cnn"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "fully convolutional networks for semantic 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)