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: "mining mid level visual patterns with deep cnn activations"
  Abstract: The purpose of mid-level visual element discovery is to find clusters of image patches that are both representative and discriminative. Here we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). We observe that a fully-connected CNN activation extracted from an image patch typically possesses two appealing properties that enable its seamless integration with pattern mining techniques. The marriage between CNN activations and association rule mining, a well-known pattern mining technique in the literature, leads to fast and effective discovery of representative and discriminative patterns from a huge number of image patches. When we retrieve and visualize image patches with the same pattern, surprisingly, they are not only visually similar but also semantically consistent, and thus give rise to a mid-level visual element in our work. Given the patterns and retrieved mid-level visual elements, we propose two methods to generate image feature representations for each. The first method is to use the patterns as codewords in a dictionary, similar to the Bag-of-Visual-Words model, we compute a Bag-of-Patterns representation. The second one relies on the retrieved mid-level visual elements to construct a Bag-of-Elements representation. We evaluate the two encoding methods on scene and object classification tasks, and demonstrate that our approach outperforms or matches recent works using CNN activations for these tasks.

Connected through hyperedge HE-1 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "harmonious attention network for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-4 (induced by source paper "modeling visual compatibility through hierarchical mid level elements"):
  with members:
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "unsupervised visual representation learning by context prediction"
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "clothing co parsing by joint image segmentation and labeling"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "cross convolutional layer pooling for image recognition"):
  with members:
    - "texture synthesis using shallow convolutional networks with random filters"
    - "bilinear cnn models for fine grained visual recognition"
    - "mid level deep pattern mining"
    - "fast r cnn"
    - "texture synthesis using convolutional neural networks"
    - "aggregating deep convolutional features for image retrieval"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "very deep convolutional networks for large scale image recognition"

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)