human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the usfd spoken language translation system for iwslt 2014"
  Abstract: The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.

Connected through hyperedge HE-1 (induced by source paper "automatic genre and show identification of broadcast media"):
  with members:
    - "unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition"
    - "latent dirichlet allocation based organisation of broadcast media archives for deep neural network adaptation"
    - "data selective transfer learning for multi domain speech recognition"

Connected through hyperedge HE-2 (induced by source paper "latent dirichlet allocation based organisation of broadcast media archives for deep neural network adaptation"):
  with members:
    - "unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition"
    - "the 2015 sheffield system for transcription of multi genre broadcast media"
    - "data selective transfer learning for multi domain speech recognition"
    - "background tracking acoustic features for genre identification of broadcast shows"

Connected through hyperedge HE-3 (induced by source paper "unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition"):
  with members:
    - "background tracking acoustic features for genre identification of broadcast shows"

Connected through hyperedge HE-4 (induced by source paper "data selective transfer learning for multi domain speech recognition"):
  with members:
    - "background tracking acoustic features for genre identification of broadcast shows"

Question: Please tell me which class the center node 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)