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: "improving domain adaptation translation with domain invariant and specific information"
  Abstract: In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.

Connected through hyperedge HE-1 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "domain adversarial training for accented speech recognition"
    - "translating pro drop languages with reconstruction models"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "iterative dual domain adaptation for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "non parametric adaptation for neural machine translation"
    - "semantic neural machine translation using amr"
    - "a survey of domain adaptation for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "multi domain neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "revisiting simple domain adaptation methods in unsupervised neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"

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)