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 "speech2vec a sequence to sequence framework for learning word embeddings from speech"):
  Abstract: In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions.

Includes members:
  Member "learning general purpose distributed sentence representations via large scale multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "supervised and unsupervised transfer learning for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering")
  Member "a segmental framework for fully unsupervised large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "neural amr sequence to sequence models for parsing and generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "speech based visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal reasoning via audio question answering")
  Member "character composition model with convolutional neural networks for dependency parsing on morphologically rich languages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection")
  Member "learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders")

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)