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: "sequential convolutional neural networks for slot filling in spoken language understanding"
  Abstract: We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding. We propose a novel CNN architecture for sequence labeling which takes into account the previous context words with preserved order information and pays special attention to the current word with its surrounding context. Moreover, it combines the information from the past and the future words for classification. Our proposed CNN architecture outperforms even the previously best ensembling recurrent neural network model and achieves state-of-the-art results with an F1-score of 95.61% on the ATIS benchmark dataset without using any additional linguistic knowledge and resources.

Connected through hyperedge HE-1 (induced by source paper "improving slot filling by utilizing contextual information"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "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"
    - "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 "bert for joint intent classification and slot filling"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "adversarial multi task learning for text classification"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-3 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"

Connected through hyperedge HE-4 (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"
    - "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"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-5 (induced by source paper "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "asr error management for improving spoken language understanding"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reading wikipedia to answer open domain questions"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "hierarchical attention model for improved machine comprehension of spoken content"

Connected through hyperedge HE-6 (induced by source paper "multi layer ensembling techniques for multilingual intent classification"):
  with members:
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "neural machine translation by jointly learning to align and translate"
    - "representing text chunks"

Connected through hyperedge HE-7 (induced by source paper "pytext a seamless path from nlp research to production"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-8 (induced by source paper "actigraphy based sleep wake pattern detection using convolutional neural networks"):
  with members:
    - "deep convolutional and recurrent models for human activity recognition using wearables"

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)