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: "automatic discovery and optimization of parts for image classification"
  Abstract: Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vector of part responses. In this paper we unify the two stages and learn the image classifiers and a set of shared parts jointly. We generate an initial pool of parts by randomly sampling part candidates and selecting a good subset using L1/L2 regularization. All steps are driven "directly" by the same objective namely the classification loss on a training set. This lets us do away with engineered heuristics. We also introduce the notion of "negative parts", intended as parts that are negatively correlated with one or more classes. Negative parts are complementary to the parts discovered by other methods, which look only for positive correlations.

Connected through hyperedge HE-1 (induced by source paper "min max entropy for weakly supervised pointwise localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"

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 "an overview of computational approaches for interpretation analysis"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "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"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-4 (induced by source paper "parameter free spatial attention network for person re identification"):
  with members:
    - "sharp attention network via adaptive sampling for person re identification"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "random erasing data augmentation"
    - "squeeze and excitation networks"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-5 (induced by source paper "deep patch learning for weakly supervised object classification and discovery"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "fast r cnn"
    - "relaxed multiple instance svm with application to object discovery"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "exploit bounding box annotations for multi label object recognition"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"

Connected through hyperedge HE-6 (induced by source paper "expanded parts model for semantic description of humans in still images"):
  with members:
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deeppose human pose estimation via deep neural networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (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"
    - "mid level deep pattern mining"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "patternnet visual pattern mining with deep neural network"):
  with members:
    - "mid level deep pattern mining"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "part based r cnns for fine grained category detection"
    - "attention for fine grained categorization"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and 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)