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: "contrastive unsupervised word alignment with non local features"
  Abstract: Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not only introduces prior knowledge to guide unsupervised learning but also cancels out partition functions. Based on the observation that the probability mass of log-linear models for word alignment is usually highly concentrated, we propose to use top-n alignments to approximate the expectations with respect to posterior distributions. This allows for efficient and accurate calculation of expectations of non-local features. Experiments show that our approach achieves significant improvements over state-of-the-art unsupervised word alignment methods.

Connected through hyperedge HE-1 (induced by source paper "understanding neural machine translation by simplification the case of encoder free models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

Connected through hyperedge HE-2 (induced by source paper "automatic testing and improvement of machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "modeling recurrence for transformer"
    - "robust neural machine translation with doubly adversarial inputs"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-3 (induced by source paper "variational recurrent neural machine translation"):
  with members:
    - "a gru gated attention model for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "deep neural machine translation with linear associative unit"
    - "memory enhanced decoder for neural machine translation"
    - "variational neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with key value memory augmented attention"):
  with members:
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "memory augmented neural machine translation"
    - "search engine guided non parametric neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "memory enhanced decoder for neural machine translation"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "coverage embedding models for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "modeling past and future for neural machine translation"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "frustratingly short attention spans in neural language modeling"
    - "stronger baselines for trustable results in neural machine translation"
    - "learning to decode for future success"
    - "neural machine translation with word predictions"
    - "massive exploration of neural machine translation architectures"
    - "neural machine translation advised by statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "refining source representations with relation networks for neural machine translation"):
  with members:
    - "variational recurrent neural machine translation"
    - "learning to parse and translate improves neural machine translation"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "modeling source syntax for neural machine translation"):
  with members:
    - "neural machine translation advised by statistical machine translation"
    - "tree to sequence attentional neural machine translation"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"
    - "modeling coverage for neural machine translation"
    - "factored neural machine translation"
    - "supervised attentions for neural machine translation"
    - "linguistic input features improve neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation with supervised attention"):
  with members:
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "attention correctness in neural image captioning"
    - "guided alignment training for topic aware neural machine translation"
    - "modeling coverage for neural machine translation"
    - "supervised attentions for neural machine translation"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "incorporating discrete translation lexicons into 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)