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: "sequence to sequence data augmentation for dialogue language understanding"
  Abstract: In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance's same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multidomain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.

Connected through hyperedge HE-1 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-2 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"

Connected through hyperedge HE-3 (induced by source paper "coreference resolution as query based span prediction"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "ellipsis and coreference resolution as question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "entity relation extraction as multi turn question answering"
    - "bert for coreference resolution baselines and analysis"
    - "gender bias in contextualized word embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "a closer look at feature space data augmentation for few shot intent classification"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "data augmentation with atomic templates for spoken language understanding"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "few shot learning with meta metric learners"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "active learning for new domains in natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "automatically learning data augmentation policies for dialogue tasks"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "toward controlled generation of text"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"
    - "dual supervised learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"

Connected through hyperedge HE-6 (induced by source paper "effective data augmentation approaches to end to end task oriented dialogue"):
  with members:
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "talking to myself self dialogues as data for conversational agents"
    - "automatic data expansion for customer care spoken language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "data augmentation with atomic templates for spoken language understanding"):
  with members:
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "zero shot adaptive transfer for conversational language understanding"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "concept transfer learning for adaptive language understanding"
    - "pointing the unknown words"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

Connected through hyperedge HE-8 (induced by source paper "conditioned query generation for task oriented dialogue systems"):
  with members:
    - "efficient semi supervised learning for natural language understanding by optimizing diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "spoken language understanding on the edge"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "texygen a benchmarking platform for text generation models"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarially regularized autoencoders"
    - "the concrete distribution a continuous relaxation of discrete random variables"

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)