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: "incorporating subword information into matrix factorization word embeddings"
  Abstract: The positive effect of adding subword information to word embeddings has been demonstrated for predictive models. In this paper we investigate whether similar benefits can also be derived from incorporating subwords into counting models. We evaluate the impact of different types of subwords (n-grams and unsupervised morphemes), with results confirming the importance of subword information in learning representations of rare and out-of-vocabulary words.

Connected through hyperedge HE-1 (induced by source paper "bertram improved word embeddings have big impact on contextualized model performance"):
  with members:
    - "bad form comparing context based and form based few shot learning in distributional semantic models"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "rare words a major problem for contextualized embeddings and how to fix it by attentive mimicking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic representations for novel words leveraging both form and context"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "rare words a major problem for contextualized embeddings and how to fix it by attentive mimicking"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "huggingface s transformers state of the art natural language processing"
    - "learning semantic representations for novel words leveraging both form and context"
    - "the natural language decathlon multitask learning as question answering"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "attentive mimicking better word embeddings by attending to informative contexts"):
  with members:
    - "evaluating word embeddings in multi label classification using fine grained name typing"
    - "learning semantic representations for novel words leveraging both form and context"
    - "attention is all you need"
    - "mimicking word embeddings using subword rnns"
    - "high risk learning acquiring new word vectors from tiny data"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-4 (induced by source paper "object and text guided semantics for cnn based activity recognition"):
  with members:
    - "iod cnn integrating object detection networks for event recognition"
    - "exploitation of semantic keywords for malicious event classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "bag of tricks for efficient text classification"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "an all in one convolutional neural network for face analysis"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "learning semantic representations for novel words leveraging both form and context"):
  with members:
    - "self attention with relative position representations"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "mimicking word embeddings using subword rnns"
    - "high risk learning acquiring new word vectors from tiny data"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "efficient estimation of word representations in vector space"

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)