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: "mid level deep pattern mining"
  Abstract: Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "informative visual storytelling with cross modal rules"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "image captioning with semantic attention"
    - "rare association rule mining for network intrusion detection"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-4 (induced by source paper "an overview of computational approaches for analyzing interpretation"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "multimodal social media analysis for gang violence prevention"
    - "adversarial defense based on structure to signal autoencoders"
    - "what do deep networks like to see"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-5 (induced by source paper "semi supervised co analysis of 3d shape styles from projected lines"):
  with members:
    - "data driven shape analysis and processing"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "mid level elements for object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-6 (induced by source paper "viewpoint aware video summarization"):
  with members:
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "query focused extractive video summarization"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "unsupervised discovery of mid level discriminative patches"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"):
  with members:
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "what makes imagenet good for transfer learning"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised visual representation learning by context prediction"
    - "webly supervised learning of convolutional networks"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "exploiting local features from deep networks for image retrieval"

Connected through hyperedge HE-8 (induced by source paper "ensemble of part detectors for simultaneous classification and localization"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "weakly supervised deep detection networks"
    - "very deep convolutional networks for large scale image recognition"
    - "automatic discovery and optimization of parts for image classification"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "visualizing and understanding convolutional 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)