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 "generating diverse translation by manipulating multi head attention"):
  Abstract: Transformer model has been widely used on machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder multi-head attention: different attention heads of the final decoder layer align to different word translation candidates. We empirically verify this discovery and propose a method to generate diverse translations by manipulating heads. Furthermore, we make use of these diverse translations with the back-translation technique for better data augmentation. Experiment results show that our method generates diverse translations without severe drop in translation quality. Experiments also show that back-translation with these diverse translations could bring significant improvement on performance on translation tasks. An auxiliary experiment of conversation response generation task proves the effect of diversity as well.

Includes members:
  Member "information aggregation for multi head attention with routing by agreement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assessing the ability of self attention networks to learn word order")
  Member "mixture models for diverse machine translation tricks of the trade":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the discrepancy between density estimation and sequence generation")
  Member "investigating backtranslation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "d page diverse paraphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards diverse paraphrase generation using multi class wasserstein gan")
  Member "multi head attention with disagreement regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "assessing the ability of self attention networks to learn word order")
  Member "convolutional self attention network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention augmented convolutional networks")
  Member "sequence to sequence mixture model for diverse machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised data augmentation for consistency training")
  Member "generating more interesting responses in neural conversation models with distributional constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non autoregressive neural dialogue generation")

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)