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: "linear ensembles of word embedding models"
  Abstract: This paper explores linear methods for combining several word embedding models into an ensemble. We construct the combined models using an iterative method based on either ordinary least squares regression or the solution to the orthogonal Procrustes problem. #R##N#We evaluate the proposed approaches on Estonian---a morphologically complex language, for which the available corpora for training word embeddings are relatively small. We compare both combined models with each other and with the input word embedding models using synonym and analogy tests. The results show that while using the ordinary least squares regression performs poorly in our experiments, using orthogonal Procrustes to combine several word embedding models into an ensemble model leads to 7-10% relative improvements over the mean result of the initial models in synonym tests and 19-47% in analogy tests.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-2 (induced by source paper "hierarchical meta embeddings for code switching named entity recognition"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "code switching language modeling using syntax aware multi task learning"
    - "dynamic meta embeddings for improved sentence representations"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "learning word vectors for 157 languages"
    - "think globally embed locally locally linear meta embedding of words"

Connected through hyperedge HE-3 (induced by source paper "cross domain ambiguity detection using linear transformation of word embedding spaces"):
  with members:
    - "a wind of change detecting and evaluating lexical semantic change across times and domains"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-4 (induced by source paper "distilled wasserstein learning for word embedding and topic modeling"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "progressive neural networks"
    - "topic compositional neural language model"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "regularizing neural networks by penalizing confident output distributions"

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)