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 "multilingual acoustic word embedding models for processing zero resource languages"):
  Abstract: Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training--testing language pair.

Includes members:
  Member "learned in speech recognition contextual acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised speaker embedding de mixing in two speaker environment")
  Member "audio linguistic embeddings for spoken sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space")
  Member "bayesian subspace hidden markov model for acoustic unit discovery":
    also connected through other hyperedges:
      (none)
  Member "massively multilingual adversarial speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks")
  Member "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speechbert cross modal pre trained language model for end to end spoken question answering")
  Member "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "phonetic and semantic embedding of spoken words with applications in spoken content retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models")
  Member "truly unsupervised acoustic word embeddings using weak top down constraints in encoder decoder models":
    also connected through other hyperedges:
      (none)

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)