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: "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
  Abstract: This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

Connected through hyperedge HE-1 (induced by source paper "joint contextual modeling for asr correction and language understanding"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "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"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "improving semantic parsing for task oriented dialog"):
  with members:
    - "deep contextualized word representations"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "learning an executable neural semantic parser"
    - "sling a framework for frame semantic parsing"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "character level feature extraction with densely connected networks"):
  with members:
    - "neural models for sequence chunking"
    - "don t decay the learning rate increase the batch size"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-4 (induced by source paper "submodularity inspired data selection for goal oriented chatbot training based on sentence embeddings"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "neural models for sequence chunking"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "neural models for sequence chunking"):
  with members:
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "recurrent neural networks with external memory for language understanding"

Connected through hyperedge HE-6 (induced by source paper "concept transfer learning for adaptive language understanding"):
  with members:
    - "neural models for sequence chunking"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "recurrent neural networks with external memory for language understanding"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "multi domain adversarial learning for slot filling in spoken language understanding"):
  with members:
    - "aspect augmented adversarial networks for domain adaptation"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "recurrent neural networks with external memory for language understanding"
    - "frustratingly easy domain adaptation"

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)