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: "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
  Abstract: Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii) domain/dialect dependent. We explore three language-independent alternatives to morphological segmentation using: i) data-driven sub-word units, ii) characters as a unit of learning, and iii) word embeddings learned using a character CNN (Convolution Neural Network). On the tasks of Machine Translation and POS tagging, we found these methods to achieve close to, and occasionally surpass state-of-the-art performance. In our analysis, we show that a neural machine translation system is sensitive to the ratio of source and target tokens, and a ratio close to 1 or greater, gives optimal performance.

Connected through hyperedge HE-1 (induced by source paper "pointer based fusion of bilingual lexicons into neural machine translation"):
  with members:
    - "code switching for enhancing nmt with pre specified translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"

Connected through hyperedge HE-2 (induced by source paper "incremental decoding and training methods for simultaneous translation in neural machine translation"):
  with members:
    - "neural machine translation training in a multi domain scenario"
    - "can neural machine translation do simultaneous translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (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"
    - "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"
    - "large scale machine translation between arabic and hebrew available corpora and initial results"

Connected through hyperedge HE-5 (induced by source paper "neural machine translation training in a multi domain scenario"):
  with members:
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "domain specialization a post training domain adaptation for neural machine translation"
    - "an arabic hebrew parallel corpus of ted talks"
    - "edinburgh neural machine translation systems for wmt 16"
    - "neural machine translation of rare words with subword units"

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)