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: "fine grained human evaluation of neural versus phrase based machine translation"
  Abstract: We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.

Connected through hyperedge HE-1 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"

Connected through hyperedge HE-2 (induced by source paper "what level of quality can neural machine translation attain on literary text"):
  with members:
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural versus phrase based machine translation quality a case study"
    - "edinburgh neural machine translation systems for wmt 16"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-3 (induced by source paper "quantitative fine grained human evaluation of machine translation systems a case study on english to croatian"):
  with members:
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "neural versus phrase based machine translation quality a case study"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "a user study on online adaptation of neural machine translation to human post edits"):
  with members:
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "quantitative fine grained human evaluation of machine translation systems a case study on english to croatian"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "can neural machine translation be improved with user feedback"
    - "a challenge set approach to evaluating machine translation"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "six challenges for neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

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)