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: "deep extreme multi label learning"
  Abstract: Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result.

Connected through hyperedge HE-1 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification"):
  with members:
    - "hyperbolic attention networks"
    - "combined convolutional and recurrent neural networks for hierarchical classification of images"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "joint embedding of words and labels for text classification"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-2 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "neural semantic encoders"

Connected through hyperedge HE-3 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "contextual action recognition with r cnn"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-4 (induced by source paper "ldsm logarithm depth streaming multi label decision trees"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "logarithmic time online multiclass prediction"
    - "normalized online learning"
    - "large scale multi label learning with missing labels"
    - "the landmark selection method for multiple output prediction"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "an embarrassingly simple baseline for extreme multi label prediction"):
  with members:
    - "single pass pca of large high dimensional data"
    - "experiments with random projection"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-6 (induced by source paper "using deep learning for title based semantic subject indexing to reach competitive performance to full text"):
  with members:
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "do convolutional networks need to be deep for text classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "using titles vs full text as source for automated semantic document annotation"
    - "multichannel variable size convolution for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "leveraging distributional semantics for multi label learning"):
  with members:
    - "document embedding with paragraph vectors"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label learning with missing labels"

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)