human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient intent detection with dual sentence encoders"):
  Abstract: Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and ConveRT. We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings. In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset comprising 13,083 annotated examples over 77 intents.

Includes members:
  Member "q8bert quantized 8bit bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search")
  Member "distilbert a distilled version of bert smaller faster cheaper and lighter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "training neural response selection for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pre training for query rewriting in a spoken language understanding system")
  Member "pretraining methods for dialog context representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "to tune or not to tune adapting pretrained representations to diverse tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "a repository of conversational datasets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convert efficient and accurate conversational representations from transformers")
  Member "zero shot cross lingual dialogue systems with transferable latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "towards scalable multi domain conversational agents the schema guided dialogue dataset":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems")

Question: Please tell me which class the source paper 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)