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 "on the difficulty of a distributional semantics of spoken language"):
  Abstract: The bulk of research in the area of speech processing concerns itself with supervised approaches to transcribing spoken language into text. In the domain of unsupervised learning most work on speech has focused on discovering relatively low level constructs such as phoneme inventories or word-like units. This is in contrast to research on written language, where there is a large body of work on unsupervised induction of semantic representations of words and whole sentences and longer texts. In this study we examine the challenges of adapting these approaches from written to spoken language. We conjecture that unsupervised learning of spoken language semantics becomes possible if we abstract from the surface variability. We simulate this setting by using a dataset of utterances spoken by a realistic but uniform synthetic voice. We evaluate two simple unsupervised models which, to varying degrees of success, learn semantic representations of speech fragments. Finally we suggest possible routes toward transferring our methods to the domain of unrestricted natural speech.

Includes members:
  Member "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "visually grounded learning of keyword prediction from untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "encoding of phonology in a recurrent neural model of grounded speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "representations of language in a model of visually grounded speech signal":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "neural discrete representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders")
  Member "self adaptive hierarchical sentence model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth adaptive graph recurrent network for text classification")

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)