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 "classifying topics in speech when all you have is crummy translations"):
  Abstract: Given a large amount of unannotated speech in a language with few resources, can we classify the speech utterances by topic? We show that this is possible if text translations are available for just a small amount of speech (less than 20 hours), using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify 1-minute speech segments over 70% of the time - a 20% improvement over a majority-class baseline. Such a system might be useful for humanitarian applications like crisis response, where incoming speech must be quickly assessed for further action.

Includes members:
  Member "visually grounded cross lingual keyword spotting in speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "query by example on device keyword spotting")
  Member "towards speech to text translation without speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "learning topic models going beyond svd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contrastive estimation reveals topic posterior information to linear models")

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)