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: "unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition"
  Abstract: Speech recognition systems are often highly domain dependent, a fact widely reported in the literature. However the concept of domain is complex and not bound to clear criteria. Hence it is often not evident if data should be considered to be out-of-domain. While both acoustic and language models can be domain specific, work in this paper concentrates on acoustic modelling. We present a novel method to perform unsupervised discovery of domains using Latent Dirichlet Allocation (LDA) modelling. Here a set of hidden domains is assumed to exist in the data, whereby each audio segment can be considered to be a weighted mixture of domain properties. The classification of audio segments into domains allows the creation of domain specific acoustic models for automatic speech recognition. Experiments are conducted on a dataset of diverse speech data covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech, with a joint training set of 60 hours and a test set of 6 hours. Maximum A Posteriori (MAP) adaptation to LDA based domains was shown to yield relative Word Error Rate (WER) improvements of up to 16% relative, compared to pooled training, and up to 10%, compared with models adapted with human-labelled prior domain knowledge.

Connected through hyperedge HE-1 (induced by source paper "latent dirichlet allocation based acoustic data selection for automatic speech recognition"):
  with members:
    - "automatic genre and show identification of broadcast media"
    - "the 2015 sheffield system for transcription of multi genre broadcast media"
    - "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 "automatic genre and show identification of broadcast media"):
  with members:
    - "latent dirichlet allocation based organisation of broadcast media archives for deep neural network adaptation"
    - "the usfd spoken language translation system for iwslt 2014"
    - "data selective transfer learning for multi domain speech recognition"

Connected through hyperedge HE-3 (induced by source paper "latent dirichlet allocation based organisation of broadcast media archives for deep neural network adaptation"):
  with members:
    - "the 2015 sheffield system for transcription of multi genre broadcast media"
    - "the usfd spoken language translation system for iwslt 2014"
    - "data selective transfer learning for multi domain speech recognition"
    - "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)