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: "a survey of word reordering in statistical machine translation computational models and language phenomena"
  Abstract: Word reordering is one of the most difficult aspects of statistical machine translation SMT, and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials.#R##N##R##N#To orient the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling.#R##N##R##N#We then question why some approaches are more successful than others in different language pairs. We argue that besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.

Connected through hyperedge HE-1 (induced by source paper "handling syntactic divergence in low resource machine translation"):
  with members:
    - "density matching for bilingual word embedding"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "learning word vectors for 157 languages"
    - "rapid adaptation of neural machine translation to new languages"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-2 (induced by source paper "low resource syntactic transfer with unsupervised source reordering"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "neural machine translation for low resource languages"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "deep biaffine attention for neural dependency parsing"
    - "cross lingual syntactic transfer with limited resources"
    - "many languages one parser"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "machine translation using semantic web technologies a survey"):
  with members:
    - "lidioms a multilingual linked idioms data set"
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "towards an arabic english machine translation based on semantic web"
    - "six challenges for neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "word2vec vs dbnary augmenting meteor using vector representations or lexical resources"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with supervised attention"):
  with members:
    - "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"
    - "neural reranking improves subjective quality of machine translation naist at wat2015"

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)