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 sub structure in neural machine translation"):
  Abstract: Current approaches to machine translation (MT) either translate sentences in isolation, disregarding the context they appear in, or model context on the level of the full document, without a notion of any internal structure the document may have. In this work we consider the fact that documents are rarely homogeneous blocks of text, but rather consist of parts covering different topics. Some documents, e.g. biographies and encyclopedia entries have highly predictable, regular structures in which sections are characterised by different topics. We draw inspiration from Louis and Webber (2014) who use this information to improve MT and transfer their proposal into the framework of neural MT. We compare two different methods of including information about the topic of the section within which each sentence is found: one using side constraints and the other using a cache-based model. We create and release the data on which we run our experiments -- parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies, preserving the boundaries of sections within the articles.

Includes members:
  Member "tagged back translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tag less back translation")
  Member "the effect of translationese in machine translation test sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the evaluation of machine translation systems trained with back translation")
  Member "selective attention for context aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward making the most of context in neural machine translation")
  Member "translationese in machine translation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "improving the transformer translation model with document level context":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "toward making the most of context in neural machine translation")
  Member "modeling coherence for neural machine translation with dynamic and topic caches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  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)