human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "synthetic and natural noise both break neural machine translation"):
  Abstract: Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

Includes members:
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deep text classification can be fooled":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "towards crafting text adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pointer based fusion of bilingual lexicons into neural machine translation")
  Member "what do neural machine translation models learn about morphology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "crafting adversarial input sequences for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robustness verification for transformers")

Question: Please tell me which class the source paper 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)