human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "neural machine translation into language varieties"
  Abstract: Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language varieties. Notable cases are standard national varieties such as Brazilian and European Portuguese, and Canadian and European French, which popular online machine translation services are not keeping distinct. We show that an evident side effect of modeling such varieties as unique classes is the generation of inconsistent translations. In this work, we investigate the problem of training neural machine translation from English to specific pairs of language varieties, assuming both labeled and unlabeled parallel texts, and low-resource conditions. We report experiments from English to two pairs of dialects, EuropeanBrazilian Portuguese and European-Canadian French, and two pairs of standardized varieties, Croatian-Serbian and Indonesian-Malay. We show significant BLEU score improvements over baseline systems when translation into similar languages is learned as a multilingual task with shared representations.

Connected through hyperedge HE-1 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "positional encoding to control output sequence length"
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "multi task neural models for translating between styles within and across languages"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "scaling neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "problems with automating translation of movie tv show subtitles"):
  with members:
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "uds dfki submission to the wmt2019 similar language translation shared task"):
  with members:
    - "a neural approach to language variety translation"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Question: Please tell me which class the center node 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)