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: "bi directional differentiable input reconstruction for low resource neural machine translation"
  Abstract: We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). We reconstruct the input from sampled translations and leverage differentiable sampling and bi-directional NMT to build a compact model that can be trained end-to-end. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.

Connected through hyperedge HE-1 (induced by source paper "domain robustness in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "extending defensive distillation"
    - "synthetic and natural noise both break neural machine translation"
    - "improving lexical choice in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "differentiable sampling with flexible reference word order for neural machine translation"):
  with members:
    - "a study of reinforcement learning for neural machine translation"
    - "bag of words as target for neural machine translation"
    - "deep reinforcement learning that matters"
    - "sockeye a toolkit for neural machine translation"
    - "differentiable scheduled sampling for credit assignment"
    - "searnn training rnns with global local losses"
    - "learning to compose task specific tree structures"
    - "reward augmented maximum likelihood for neural structured prediction"

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)