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: "joint slot filling and intent detection via capsule neural networks"
  Abstract: Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.

Connected through hyperedge HE-1 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "roberta a robustly optimized bert pretraining approach"
    - "exploring neural methods for parsing discourse representation structures"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "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"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-2 (induced by source paper "diversity density and homogeneity quantitative characteristic metrics for text collections"):
  with members:
    - "casa nlu context aware self attentive natural language understanding for task oriented chatbots"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "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"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "bert for joint intent classification and slot filling"
    - "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-4 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "transfer learning for context aware spoken language understanding"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "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"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-7 (induced by source paper "casa nlu context aware self attentive natural language understanding for task oriented chatbots"):
  with members:
    - "towards universal dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "jointly trained sequential labeling and classification by sparse attention neural networks"
    - "convolutional sequence to sequence learning"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "a network based end to end trainable task oriented dialogue system"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "multi lingual intent detection and slot filling in a joint bert based model"):
  with members:
    - "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"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "zero shot learning the good the bad and the ugly"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "end to end memory networks"

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)