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: "attention based recurrent neural network models for joint intent detection and slot filling"
  Abstract: Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.

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"
    - "joint slot filling and intent detection via capsule neural networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "user in the loop adaptive intent detection for instructable digital assistant"):
  with members:
    - "conversational ai the science behind the alexa prize"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a persona based neural conversation model"
    - "xgboost a scalable tree boosting system"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"

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"
    - "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"

Connected through hyperedge HE-4 (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"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-5 (induced by source paper "know thy corpus robust methods for digital curation of web corpora"):
  with members:
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-7 (induced by source paper "task oriented dialog systems that consider multiple appropriate responses under the same context"):
  with members:
    - "structured fusion networks for dialog"
    - "the curious case of neural text degeneration"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "collaborative multi agent dialogue model training via reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "counterfactual multi agent policy gradients"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

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)