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: "bert for joint intent classification and slot filling"
  Abstract: Intent classification and slot filling are two essential tasks for natural language understanding. They often suffer from small-scale human-labeled training data, resulting in poor generalization capability, especially for rare words. Recently a new language representation model, BERT (Bidirectional Encoder Representations from Transformers), facilitates pre-training deep bidirectional representations on large-scale unlabeled corpora, and has created state-of-the-art models for a wide variety of natural language processing tasks after simple fine-tuning. However, there has not been much effort on exploring BERT for natural language understanding. In this work, we propose a joint intent classification and slot filling model based on BERT. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on several public benchmark datasets, compared to the attention-based recurrent neural network models and slot-gated models.

Connected through hyperedge HE-1 (induced by source paper "rapid adaptation of bert for information extraction on domain specific business documents"):
  with members:
    - "navigation based candidate expansion and pretrained language models for citation recommendation"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "joint slot filling and intent detection via capsule neural networks"
    - "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"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"

Connected through hyperedge HE-2 (induced by source paper "joint contextual modeling for asr correction and language understanding"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learned in translation contextualized word vectors"
    - "neural language correction with character based attention"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (induced by source paper "cm net a novel collaborative memory network for spoken language understanding"):
  with members:
    - "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"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "sentence state lstm for text representation"
    - "memory augmented neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "a sketch based system for semantic parsing"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "a novel task oriented text corpus in silent speech recognition and its natural language generation construction method"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "a context aware citation recommendation model with bert and graph convolutional networks"
    - "a bert baseline for the natural questions"
    - "speech recognition with no speech or with noisy speech"
    - "bottom up abstractive summarization"
    - "sequence to sequence models for data to text natural language generation word vs character based processing and output diversity"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "improving slot filling by utilizing contextual information"):
  with members:
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "pytext a seamless path from nlp research to production"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "rasa open source language understanding and dialogue management"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"

Connected through hyperedge HE-7 (induced by source paper "a stack propagation framework with token level intent detection for spoken language understanding"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "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"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "stochastic multiple choice learning for training diverse deep ensembles"

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)