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 "learning word embeddings from speech"):
  Abstract: In this paper, we propose a novel deep neural network architecture, Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the segments, and are close to other vectors in the embedding space if their corresponding segments are semantically similar. The design of the proposed model is based on the RNN Encoder-Decoder framework, and borrows the methodology of continuous skip-grams for training. The learned vector representations are evaluated on 13 widely used word similarity benchmarks, and achieved competitive results to that of GloVe. The biggest advantage of the proposed model is its capability of extracting semantic information of audio segments taken directly from raw speech, without relying on any other modalities such as text or images, which are challenging and expensive to collect and annotate.

Includes members:
  Member "learning word like units from joint audio visual analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech")
  Member "a segmental framework for fully unsupervised large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "an embedded segmental k means model for unsupervised segmentation and clustering of speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "discriminative acoustic word embeddings recurrent neural network based approaches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "problems with evaluation of word embeddings using word similarity tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "simverb 3500 a large scale evaluation set of verb similarity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "multi view recurrent neural acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effectiveness of self supervised pre training for speech recognition")

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)