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 "improved audio embeddings by adjacency based clustering with applications in spoken term detection"):
  Abstract: Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing. Audio Word2Vec previously proposed was shown to be able to represent audio segments for spoken words as such vectors carrying information about the phonetic structures of the signal segments. However, each linguistic unit (word, syllable, phoneme in text form) corresponds to unlimited number of audio segments with vector representations inevitably spread over the embedding space, which causes some confusion. It is therefore desired to better cluster the audio embeddings such that those corresponding to the same linguistic unit can be more compactly distributed. In this paper, inspired by Siamese networks, we propose some approaches to achieve the above goal. This includes identifying positive and negative pairs from unlabeled data for Siamese style training, disentangling acoustic factors such as speaker characteristics from the audio embedding, handling unbalanced data distribution, and having the embedding processes learn from the adjacency relationships among data points. All these can be done in an unsupervised way. Improved performance was obtained in preliminary experiments on the LibriSpeech data set, including clustering characteristics analysis and applications of spoken term detection.

Includes members:
  Member "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "phonetic and semantic embedding of spoken words with applications in spoken content retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "towards unsupervised automatic speech recognition trained by unaligned speech and text only":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval")
  Member "learning acoustic word embeddings with temporal context for query by example speech search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "unsupervised learning of semantic audio representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "discriminative acoustic word embeddings recurrent neural network based approaches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "multi view recurrent neural acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effectiveness of self supervised pre training for speech recognition")
  Member "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving automated segmentation of radio shows with audio embeddings")

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)