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 "distill adapt distill training small in domain models for neural machine translation"):
  Abstract: We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.

Includes members:
  Member "go from the general to the particular multi domain translation with domain transformation networks":
    also connected through other hyperedges:
      (none)
  Member "dreaming to distill data free knowledge transfer via deepinversion":
    also connected through other hyperedges:
      (none)
  Member "iterative dual domain adaptation for neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "explaining sequence level knowledge distillation as data augmentation for neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "distilling task specific knowledge from bert into simple neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "patient knowledge distillation for bert model compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "attentive student meets multi task teacher improved knowledge distillation for pretrained models":
    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 "when and why are pre trained word embeddings useful for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pre training via leveraging assisting languages and data selection for neural machine translation")

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)