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 transfer learning for spoken language understanding in intelligent agents"
  Abstract: User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.

Connected through hyperedge HE-1 (induced by source paper "domain differential adaptation for neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "non parametric adaptation for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "zero shot user intent detection via capsule neural networks"

Connected through hyperedge HE-3 (induced by source paper "cm net a novel collaborative memory network for spoken language understanding"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "simple fast accurate intent classification and slot labeling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sentence state lstm for text representation"
    - "memory augmented neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "transfer learning for context aware spoken language understanding"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "joint slot filling and intent detection via capsule neural networks"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "an efficient approach to encoding context for spoken language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "midas a dialog act annotation scheme for open domain human machine spoken conversations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "contextual topic modeling for dialog systems"
    - "iso standard domain independent dialogue act tagging for conversational agents"
    - "advances in pre training distributed word representations"
    - "exploiting sentence and context representations in deep neural models for spoken language understanding"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "learning asr robust contextualized embeddings for spoken language understanding"):
  with members:
    - "spoken language intent detection using confusion2vec"
    - "confusion2vec towards enriching vector space word representations with representational ambiguities"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a self attention joint model for spoken language understanding in situational dialog applications"):
  with members:
    - "neural architectures for named entity recognition"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "bidirectional lstm crf models for sequence tagging"
    - "convolutional neural networks for sentence classification"

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)