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: "more accurate tests for the statistical significance of result differences"
  Abstract: Statistical significance testing of differences in values of metrics like recall, precision and balanced F-score is a necessary part of empirical natural language processing. Unfortunately, we find in a set of experiments that many commonly used tests often underestimate the significance and so are less likely to detect differences that exist between different techniques. This underestimation comes from an independence assumption that is often violated. We point out some useful tests that do not make this assumption, including computationally-intensive randomization tests.

Connected through hyperedge HE-1 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "text segmentation as a supervised learning task"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-2 (induced by source paper "comparing rule based feature based and deep neural methods for de identification of dutch medical records"):
  with members:
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer learning for named entity recognition with neural networks"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for coherence modeling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "discourse coherence in the wild a dataset evaluation and methods"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "auxiliary objectives for neural error detection models"
    - "sentence ordering and coherence modeling using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a search based neural model for biomedical nested and overlapping event detection"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning to recognize discontiguous entities"
    - "globally normalized transition based neural networks"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-6 (induced by source paper "inherent biases in reference based evaluation for grammatical error correction and text simplification"):
  with members:
    - "automatic metric validation for grammatical error correction"
    - "sentence simplification with deep reinforcement learning"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"
    - "neural language correction with character based attention"
    - "there s no comparison reference less evaluation metrics in grammatical error correction"
    - "unsupervised sentence simplification using deep semantics"

Connected through hyperedge HE-7 (induced by source paper "discourse structure in machine translation evaluation"):
  with members:
    - "discotk using discourse structure for machine translation evaluation"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "pairwise neural machine translation evaluation"
    - "the use of classifiers in sequential inference"

Connected through hyperedge HE-8 (induced by source paper "unsupervised word and dependency path embeddings for aspect term extraction"):
  with members:
    - "elixa a modular and flexible absa platform"
    - "dependency recurrent neural language models for sentence completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "unsupervised cross domain word representation learning"
    - "a dependency based neural network for relation classification"
    - "compositional vector space models for knowledge base completion"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)