human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "semi supervised multi task word embeddings"):
  Abstract: Word embeddings have been shown to benefit from ensembling several word embedding sources, often carried out using straightforward mathematical operations over the set of vectors to produce a meta-embedding representation. More recently, unsupervised learning has been used to find a lower-dimensional representation, similar in size to that of the word embeddings within the ensemble. However, these methods do not use the available manual labeled datasets that are often used solely for the purpose of evaluation. #R##N#We propose to improve word embeddings by simultaneously learning to reconstruct an ensemble of pretrained word embeddings with supervision from various labeled word similarity datasets. This involves reconstructing word meta-embeddings while simultaneously using a Siamese Network to also learn word similarity where both processes share a hidden layer. Experiments are carried out on 6 word similarity datasets and 3 analogy datasets. We find that performance is improved for all word similarity datasets when compared to unsupervised learning methods with a mean increase of 11.33 in the Spearman Correlation coefficient. Moreover, 4 of 6 of word similarity datasets from our approach show best performance when using of a cosine loss for reconstruction and Brier's loss for word similarity.

Includes members:
  Member "analogies explained towards understanding word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "product knowledge graph embedding for e commerce")
  Member "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta embeddings based on self attention")
  Member "glue a multi task benchmark and analysis platform for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "think globally embed locally locally linear meta embedding of words":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta embeddings based on self attention")
  Member "matrix factorization using window sampling and negative sampling for improved word representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a common semantic space for monolingual and cross lingual meta embeddings")
  Member "a joint model for word embedding and word morphology":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "authorship attribution in bangla literature using character level cnn")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)