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 "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  Abstract: Word embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spoken words (signal segments for words) learned from signals within spoken words. This paper proposes a two-stage framework to perform phonetic-and-semantic embedding on spoken words considering the context of the spoken words. Stage 1 performs phonetic embedding with speaker characteristics disentangled. Stage 2 then performs semantic embedding in addition. We further propose to evaluate the phonetic-and-semantic nature of the audio embeddings obtained in Stage 2 by parallelizing with text embeddings. In general, phonetic structure and semantics inevitably disturb each other. For example the words "brother" and "sister" are close in semantics but very different in phonetic structure, while the words "brother" and "bother" are in the other way around. But phonetic-and-semantic embedding is attractive, as shown in the initial experiments on spoken document retrieval. Not only spoken documents including the spoken query can be retrieved based on the phonetic structures, but spoken documents semantically related to the query but not including the query can also be retrieved based on the semantics.

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 "an iterative closest point method for unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero resource cross lingual named entity recognition")
  Member "non adversarial unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "towards unsupervised automatic speech recognition trained by unaligned speech and text only":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised cross modal alignment of speech and text embedding spaces")
  Member "speech2vec a sequence to sequence framework for learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "unsupervised adaptation with domain separation networks for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "unsupervised learning of semantic audio representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "end to end neural segmental models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "from semi supervised to almost unsupervised speech recognition with very low resource by jointly learning phonetic structures from audio and text 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)