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 "document level neural machine translation with inter sentence attention"):
  Abstract: Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods only focus on briefly introducing document-level information but fail to concern about selecting the most related part inside document context. The capacity of memory network for detecting the most relevant part of the current sentence from the memory provides a natural solution for the requirement of modeling document-level context by document-level NMT. In this work, we propose a Transformer NMT system with associated memory network (AMN) to both capture the document-level context and select the most salient part related to the concerned translation from the memory. Experiments on several tasks show that the proposed method significantly improves the NMT performance over strong Transformer baselines and other related studies.

Includes members:
  Member "semantic role labeling with associated memory network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "korean to chinese machine translation using chinese character as pivot clue")
  Member "improving the transformer translation model with document level context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "context aware neural machine translation learns anaphora resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward making the most of context in neural machine translation")
  Member "extreme adaptation for personalized neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine translation system selection from bandit feedback")
  Member "thumt an open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation")
  Member "modeling coherence for neural machine translation with dynamic and topic caches":
    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 machine translation with extended context":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "learning to remember translation history with a continuous cache":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training 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)