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: "a tutorial on principal component analysis"
  Abstract: Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.

Connected through hyperedge HE-1 (induced by source paper "cbir using features derived by deep learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "recent advance in content based image retrieval a literature survey"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "multi fusion chinese wordnet mcw compound of machine learning and manual correction"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-3 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-4 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-5 (induced by source paper "characterizing the decision boundary of deep neural networks"):
  with members:
    - "deep adversarial social recommendation"
    - "learning hierarchical discourse level structure for fake news detection"
    - "deep adversarial network alignment"
    - "on the decision boundaries of deep neural networks a tropical geometry perspective"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "investigating decision boundaries of trained neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-6 (induced by source paper "rpm net robust pixel level matching networks for self supervised video object segmentation"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "path aggregation network for instance segmentation"
    - "youtube vos a large scale video object segmentation benchmark"

Connected through hyperedge HE-7 (induced by source paper "a survey of machine learning applied to computer architecture design"):
  with members:
    - "a static analysis based cross architecture performance prediction using machine learning"
    - "optimizing routerless network on chip designs an innovative learning based framework"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "improving branch prediction by modeling global history with convolutional neural networks"
    - "learning based application agnostic 3d noc design for heterogeneous manycore systems"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the case for learned index structures"
    - "feature selection a data perspective"

Connected through hyperedge HE-8 (induced by source paper "hillview a trillion cell spreadsheet for big data"):
  with members:
    - "big data visualization tools"
    - "foresight recommending visual insights"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rapid sampling for visualizations with ordering guarantees"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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.LG(Machine Learning)