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 "semantic query by example speech search using visual grounding"):
  Abstract: A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here we consider how such models can be used for query-by-example (QbE) search, the task of retrieving utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to retrieve utterances containing exact instances of the query, but also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech segments (queries, search utterances) are mapped to fixed-dimensional embedding vectors. We show that a QbE system using an embedding function trained on visually grounded speech data outperforms a purely acoustic QbE system in terms of both exact and semantic retrieval performance.

Includes members:
  Member "semantic speech retrieval with a visually grounded model of untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "unsupervised cross modal alignment of speech and text embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "representation learning for electronic health records")
  Member "low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")
  Member "phonetic and semantic embedding of spoken words with applications in spoken content retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "visually grounded cross lingual keyword spotting in speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "speech2vec a sequence to sequence framework for learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "jointly discovering visual objects and spoken words from raw sensory input":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")

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)