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: "lepor an augmented machine translation evaluation metric"
  Abstract: Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with a low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as the source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances in different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design tunable evaluation models, ...

Connected through hyperedge HE-1 (induced by source paper "incorporating chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "neural machine translation and sequence to sequence models a tutorial"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"
    - "context gates for neural machine translation"
    - "a coverage embedding model for neural machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "otem utem over and under translation evaluation metric for nmt"):
  with members:
    - "pairwise neural machine translation evaluation"
    - "sparse and constrained attention for neural machine translation"
    - "attention is all you need"
    - "a gru gated attention model for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "incorporating structural alignment biases into an attentional neural translation model"

Connected through hyperedge HE-3 (induced by source paper "machine translation evaluation resources and methods a survey"):
  with members:
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "a survey of paraphrasing and textual entailment methods"
    - "inferring shallow transfer machine translation rules from small parallel corpora"
    - "learning to paraphrase an unsupervised approach using multiple sequence alignment"

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)