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: "the effects of data size and frequency range on distributional semantic models"
  Abstract: This paper investigates the effects of data size and frequency range on distributional semantic models. We compare the performance of a number of representative models for several test settings over data of varying sizes, and over test items of various frequency. Our results show that neural network-based models underperform when the data is small, and that the most reliable model over data of varying sizes and frequency ranges is the inverted factorized model.

Connected through hyperedge HE-1 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-2 (induced by source paper "word similarity datasets for thai construction and evaluation"):
  with members:
    - "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings"
    - "construction of a japanese word similarity dataset"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "evaluation of croatian word embeddings"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-3 (induced by source paper "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  with members:
    - "redefining context windows for word embedding models an experimental study"
    - "high risk learning acquiring new word vectors from tiny data"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "issues in evaluating semantic spaces using word analogies"
    - "mining and modeling character networks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-4 (induced by source paper "downsampling strategies are crucial for word embedding reliability"):
  with members:
    - "deep contextualized word representations"
    - "jeseme a website for exploring diachronic changes in word meaning and emotion"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep reinforcement learning that matters"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "temporal analysis of language through neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "the influence of down sampling strategies on svd word embedding stability"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"):
  with members:
    - "statistically significant detection of linguistic change"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

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)