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 "convert efficient and accurate conversational representations from transformers"):
  Abstract: General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a faster, more compact dual sentence encoder specifically optimized for dialog tasks. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. In our evaluation, we show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. #R##N#ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.

Includes members:
  Member "deep retrieval based dialogue systems a short review":
    also connected through other hyperedges:
      (none)
  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 "designing for health chatbots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a repository of conversational datasets")
  Member "are sixteen heads really better than one":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "dialog system technology challenge 7":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conversing by reading contentful neural conversation with on demand machine reading")
  Member "distilbert a distilled version of bert smaller faster cheaper and lighter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ma dst multi attention based scalable dialog state tracking")
  Member "extreme language model compression with optimal subwords and shared projections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search")

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)