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 "modelling latent skills for multitask language generation"):
  Abstract: We present a generative model for multitask conditional language generation. Our guiding hypothesis is that a shared set of latent skills underlies many disparate language generation tasks, and that explicitly modelling these skills in a task embedding space can help with both positive transfer across tasks and with efficient adaptation to new tasks. We instantiate this task embedding space as a latent variable in a latent variable sequence-to-sequence model. We evaluate this hypothesis by curating a series of monolingual text-to-text language generation datasets - covering a broad range of tasks and domains - and comparing the performance of models both in the multitask and few-shot regimes. We show that our latent task variable model outperforms other sequence-to-sequence baselines on average across tasks in the multitask setting. In the few-shot learning setting on an unseen test dataset (i.e., a new task), we demonstrate that model adaptation based on inference in the latent task space is more robust than standard fine-tuning based parameter adaptation and performs comparably in terms of overall performance. Finally, we examine the latent task representations learnt by our model and show that they cluster tasks in a natural way.

Includes members:
  Member "plug and play language models a simple approach to controlled text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "guiding extractive summarization with question answering rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding generalizable evidence by learning to convince q a models")
  Member "continual unsupervised representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "leafnats an open source toolkit and live demo system for neural abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization")
  Member "pegasus pre training with extracted gap sentences for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "putting machine translation in context with the noisy channel model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "neural extractive text summarization with syntactic compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural text summarization a critical evaluation")
  Member "text summarization with pretrained encoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")

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)