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: "data selective transfer learning for multi domain speech recognition"
  Abstract: Negative transfer in training of acoustic models for automatic speech recognition has been reported in several contexts such as domain change or speaker characteristics. This paper proposes a novel technique to overcome negative transfer by efficient selection of speech data for acoustic model training. Here data is chosen on relevance for a specific target. A submodular function based on likelihood ratios is used to determine how acoustically similar each training utterance is to a target test set. The approach is evaluated on a wide–domain data set, covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech. Experiments demonstrate that the proposed technique both finds relevant data and limits negative transfer. Results on a 6–hour test set show a relative improvement of 4% with data selection over using all data in PLP based models, and 2% with DNN features.

Connected through hyperedge HE-1 (induced by source paper "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "adversarial domain adaptation for duplicate question detection"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "unsupervised domain discovery using latent dirichlet allocation for acoustic modelling in speech recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "two stage synthesis networks for transfer learning in machine comprehension"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "semi supervised qa with generative domain adaptive nets"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "squad 100 000 questions for machine comprehension of text"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "supervised and unsupervised transfer learning for question answering"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "aspect augmented adversarial networks for domain adaptation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "neural domain adaptation for biomedical question answering"
    - "query based attention cnn for text similarity map"
    - "learning word embeddings from speech"
    - "newsqa a machine comprehension dataset"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Connected through hyperedge HE-5 (induced by source paper "personalized acoustic modeling by weakly supervised multi task deep learning using acoustic tokens discovered from unlabeled data"):
  with members:
    - "unsupervised spoken term detection with spoken queries by multi level acoustic patterns with varying model granularity"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"

Connected through hyperedge HE-6 (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"
    - "the usfd spoken language translation system for iwslt 2014"

Connected through hyperedge HE-7 (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"
    - "the usfd spoken language translation system for iwslt 2014"
    - "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.LG(Machine Learning)