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 no regret generalization of hierarchical softmax to extreme multi label classification"
  Abstract: Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach commonly used for multi-class problems. In this paper, we investigate probabilistic label trees (PLTs) that have been recently devised for tackling XMLC problems. We show that PLTs are a no-regret multi-label generalization of HSM when precision@k is used as a model evaluation metric. Critically, we prove that pick-one-label heuristic - a reduction technique from multi-label to multi-class that is routinely used along with HSM - is not consistent in general. We also show that our implementation of PLTs, referred to as extremeText (XT), obtains significantly better results than HSM with the pick-one-label heuristic and XML-CNN, a deep network specifically designed for XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches in terms of statistical performance, model size and prediction time which makes it amenable to deploy in an online system.

Connected through hyperedge HE-1 (induced by source paper "large scale multi label text classification on eu legislation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "explainable prediction of medical codes from clinical text"
    - "deep contextualized word representations"
    - "obligation and prohibition extraction using hierarchical rnns"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural machine translation in linear time"

Connected through hyperedge HE-2 (induced by source paper "bonsai diverse and shallow trees for extreme multi label classification"):
  with members:
    - "accelerating extreme classification via adaptive feature agglomeration"
    - "ldsm logarithm depth streaming multi label decision trees"
    - "block wise partitioning for extreme multi label classification"
    - "bag of tricks for efficient text classification"
    - "lshtc a benchmark for large scale text classification"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "ldsm logarithm depth streaming multi label decision trees"):
  with members:
    - "bonsai diverse and shallow trees for 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-4 (induced by source paper "on the computational complexity of the probabilistic label tree algorithms"):
  with members:
    - "conditional probability tree estimation analysis and algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "error correcting tournaments"

Connected through hyperedge HE-5 (induced by source paper "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"):
  with members:
    - "bonsai diverse and shallow trees for 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-6 (induced by source paper "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "sgm sequence generation model for 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)