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 "multi granularity self attention for neural machine translation"):
  Abstract: Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting the possibility of improving NMT performance from explicit modeling of phrases. In this work, we present multi-granularity self-attention (Mg-Sa): a neural network that combines multi-head self-attention and phrase modeling. Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic formalism. Moreover, we exploit interactions among phrases to enhance the strength of structure modeling - a commonly-cited weakness of self-attention. Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves performance. Targeted linguistic analysis reveals that Mg-Sa indeed captures useful phrase information at various levels of granularities.

Includes members:
  Member "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "area attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "separate and attend in personal email search")
  Member "information aggregation for multi head attention with routing by agreement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessing the ability of self attention networks to learn word order")
  Member "strategies for structuring story generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "towards better modeling hierarchical structure for self attention with ordered neurons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic business process structure discovery using ordered neurons lstm a preliminary study")
  Member "syntax enhanced neural machine translation with syntax aware word representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing")
  Member "modeling recurrence for transformer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "phrase based attentions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural phrase to phrase 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)