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: "subword semantic hashing for intent classification on small datasets"
  Abstract: In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks. Intent Classification on a small dataset is a challenging task for data-hungry state-of-the-art Deep Learning based systems. Semantic Hashing is an attempt to overcome such a challenge and learn robust text classification. Current word embedding based are dependent on vocabularies. One of the major drawbacks of such methods is out-of-vocabulary terms, especially when having small training datasets and using a wider vocabulary. This is the case in Intent Classification for chatbots, where typically small datasets are extracted from internet communication. Two problems arise by the use of internet communication. First, such datasets miss a lot of terms in the vocabulary to use word embeddings efficiently. Second, users frequently make spelling errors. Typically, the models for intent classification are not trained with spelling errors and it is difficult to think about ways in which users will make mistakes. Models depending on a word vocabulary will always face such issues. An ideal classifier should handle spelling errors inherently. With Semantic Hashing, we overcome these challenges and achieve state-of-the-art results on three datasets: AskUbuntu, Chatbot, and Web Application. Our benchmarks are available online: this https URL

Connected through hyperedge HE-1 (induced by source paper "hyperembed tradeoffs between resources and performance in nlp tasks with hyperdimensional computing enabled embedding of n gram statistics"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "neural random projections for language modelling"
    - "handling massive n gram datasets efficiently"
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-2 (induced by source paper "stacked debert all attention in incomplete data for text classification"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mida multiple imputation using denoising autoencoders"
    - "attention is all you need"
    - "convolutional neural networks for sentence classification"
    - "show and tell a neural image caption generator"
    - "thinking is bad implications of human error research for spreadsheet research and practice"

Connected through hyperedge HE-3 (induced by source paper "question embeddings based on shannon entropy solving intent classification task in goal oriented dialogue system"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "fasttext zip compressing text classification models"
    - "bag of tricks for efficient text classification"
    - "distributed representations of sentences and documents"
    - "understanding shannon s entropy metric for information"
    - "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)