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: "machine translation using semantic web technologies a survey"
  Abstract: Abstract   A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article presents the results of a systematic review of machine translation approaches that rely on Semantic Web technologies for translating texts. Overall, our survey suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "aligning cross lingual entities with multi aspect information"):
  with members:
    - "matching entities across different knowledge graphs with graph embeddings"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "simple attention based representation learning for ranking short social media posts"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-3 (induced by source paper "extending a model for ontology based arabic english machine translation nan"):
  with members:
    - "gender aware spoken language translation applied to english arabic"
    - "machine translation history and evolution survey for arabic english translations"
    - "towards an arabic english machine translation based on semantic web"
    - "machine translation evaluation a survey"
    - "neural based machine translation for medical text domain based on european medicines agency leaflet texts"
    - "using ontology based context in the portuguese english translation of homographs in textual dialogues"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "semantic web for machine translation challenges and directions"):
  with members:
    - "augmenting neural machine translation with knowledge graphs"
    - "lidioms a multilingual linked idioms data set"
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "six challenges for 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)