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 "sparse lifting of dense vectors unifying word and sentence representations"):
  Abstract: As the first step in automated natural language processing, representing words and sentences is of central importance and has attracted significant research attention. Different approaches, from the early one-hot and bag-of-words representation to more recent distributional dense and sparse representations, were proposed. Despite the successful results that have been achieved, such vectors tend to consist of uninterpretable components and face nontrivial challenge in both memory and computational requirement in practical applications. In this paper, we designed a novel representation model that projects dense word vectors into a higher dimensional space and favors a highly sparse and binary representation of word vectors with potentially interpretable components, while trying to maintain pairwise inner products between original vectors as much as possible. Computationally, our model is relaxed as a symmetric non-negative matrix factorization problem which admits a fast yet effective solution. In a series of empirical evaluations, the proposed model exhibited consistent improvement and high potential in practical applications.

Includes members:
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "on the dimensionality of word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "leveraging term banks for answering complex questions a case for sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "spine sparse interpretable neural embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "sparse overcomplete word vector representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "learning word representations with hierarchical sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how large a vocabulary does text classification need a variational approach to vocabulary selection")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)