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: "notes on noise contrastive estimation and negative sampling"
  Abstract: Estimating the parameters of probabilistic models of language such as maxent models and probabilistic neural models is computationally difficult since it involves evaluating partition functions by summing over an entire vocabulary, which may be millions of word types in size. Two closely related strategies---noise contrastive estimation (Mnih and Teh, 2012; Mnih and Kavukcuoglu, 2013; Vaswani et al., 2013) and negative sampling (Mikolov et al., 2012; Goldberg and Levy, 2014)---have emerged as popular solutions to this computational problem, but some confusion remains as to which is more appropriate and when. This document explicates their relationships to each other and to other estimation techniques. The analysis shows that, although they are superficially similar, NCE is a general parameter estimation technique that is asymptotically unbiased, while negative sampling is best understood as a family of binary classification models that are useful for learning word representations but not as a general-purpose estimator.

Connected through hyperedge HE-1 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "stochastic answer networks for natural language inference"

Connected through hyperedge HE-2 (induced by source paper "a theoretical analysis of contrastive unsupervised representation learning"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "provable benefits of representation learning"
    - "prototypical networks for few shot learning"
    - "a non generative framework and convex relaxations for unsupervised learning"
    - "a vector contraction inequality for rademacher complexities"

Connected through hyperedge HE-3 (induced by source paper "compositional fairness constraints for graph embeddings"):
  with members:
    - "learning adversarially fair and transferable representations"
    - "adversarial contrastive estimation"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "representation learning on graphs methods and applications"
    - "on formalizing fairness in prediction with machine learning"
    - "new fairness metrics for recommendation that embrace differences"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-4 (induced by source paper "follow the leader documents on the leading edge of semantic change get more citations"):
  with members:
    - "survey of computational approaches to diachronic conceptual change"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "bounded rationality in scholarly knowledge discovery"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-5 (induced by source paper "likelihood ratios and generative classifiers for unsupervised out of domain detection in task oriented dialog"):
  with members:
    - "deep unknown intent detection with margin loss"
    - "deep anomaly detection with outlier exposure"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-6 (induced by source paper "characterization of citizens using word2vec and latent topic analysis in a large set of tweets"):
  with members:
    - "exploiting deep learning for persian sentiment analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "active community detection a maximum likelihood approach"
    - "different approaches to community detection"
    - "fasttext zip compressing text classification models"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-7 (induced by source paper "long span language modeling for speech recognition"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "language modeling with deep transformers"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "wikipedia cultural diversity dataset a complete cartography for 300 language editions"):
  with members:
    - "why the world reads wikipedia beyond english speakers"
    - "understanding editing behaviors in multilingual wikipedia"
    - "multilinguals and wikipedia editing"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"
    - "wikipedia based semantic interpretation for natural language processing"

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)