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: "bonsai diverse and shallow trees for extreme multi label classification"
  Abstract: Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a shallow tree-based algorithm, called Bonsai, which promotes diversity of the label space and easily scales to millions of labels. Bonsai relaxes the two main constraints of the recently proposed tree-based algorithm, Parabel, which partitions labels at each tree node into exactly two child nodes, and imposes label balanced-ness between these nodes. Instead, Bonsai encourages diversity in the partitioning process by (i) allowing a much larger fan-out at each node, and (ii) maintaining the diversity of the label set further by enabling potentially imbalanced partitioning. By allowing such flexibility, it achieves the best of both worlds - fast training of tree-based methods, and prediction accuracy better than Parabel, and at par with one-vs-rest methods. As a result, Bonsai outperforms state-of-the-art one-vs-rest methods such as DiSMEC in terms of prediction accuracy, while being orders of magnitude faster to train. The code for \bonsai is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "x bert extreme multi label text classification with using bidirectional encoder representations from transformers"):
  with members:
    - "haxmlnet hierarchical attention network for extreme multi label text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "stochastic negative mining for learning with large output spaces"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "ldsm logarithm depth streaming multi label decision trees"):
  with members:
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "deep extreme multi label learning"
    - "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-3 (induced by source paper "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"):
  with members:
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "averaging weights leads to wider optima and better generalization"
    - "a structured self attentive sentence embedding"
    - "lshtc a benchmark for large scale text classification"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"):
  with members:
    - "sgm sequence generation model for multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "averaging weights leads to wider optima and better generalization"
    - "bag of tricks for efficient text classification"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "enhancing navigation on wikipedia with social tags"

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)