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: "large scale machine translation between arabic and hebrew available corpora and initial results"
  Abstract: Machine translation between Arabic and Hebrew has so far been limited by a lack of parallel corpora, despite the political and cultural importance of this language pair. Previous work relied on manually-crafted grammars or pivoting via English, both of which are unsatisfactory for building a scalable and accurate MT system. In this work, we compare standard phrase-based and neural systems on Arabic-Hebrew translation. We experiment with tokenization by external tools and sub-word modeling by character-level neural models, and show that both methods lead to improved translation performance, with a small advantage to the neural models.

Connected through hyperedge HE-1 (induced by source paper "synthetic and natural noise both break neural machine translation"):
  with members:
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "towards crafting text adversarial samples"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "what do neural machine translation models learn about morphology"
    - "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "what do neural machine translation models learn about morphology"):
  with members:
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "character based neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-3 (induced by source paper "an arabic hebrew parallel corpus of ted talks"):
  with members:
    - "morphological constraints for phrase pivot statistical 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)