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 "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"):
  Abstract: Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads in the encoder to the overall performance of the model and analyze the roles played by them. We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance. For example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads results in a drop of only 0.15 BLEU.

Includes members:
  Member "the state of sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "discrete autoencoders for sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model based reinforcement learning for atari")
  Member "training tips for the transformer model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "context aware neural machine translation learns anaphora resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward making the most of context in neural machine translation")
  Member "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "the importance of being recurrent for modeling hierarchical structure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)