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: "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"
  Abstract: This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small changes in the input such as non-canonical input (e.g., typos). Yet both stability and robustness are desired properties in applications involving user-generated content, and the more as humans easily cope with such noisy or adversary conditions. In this paper, we study the impact of noisy input. We consider different noise distributions (one type of noise, combination of noise types) and mismatched noise distributions for training and testing. Moreover, we empirically evaluate the robustness of different models (convolutional neural networks, recurrent neural networks, non-neural models), different basic units (characters, byte pair encoding units), and different NLP tasks (morphological tagging, machine translation).

Connected through hyperedge HE-1 (induced by source paper "findings of the first shared task on machine translation robustness"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "ntt s machine translation systems for wmt19 robustness task"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "training on synthetic noise improves robustness to natural noise in machine translation"

Connected through hyperedge HE-2 (induced by source paper "towards robust toxic content classification"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "challenges for toxic comment classification an in depth error analysis"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "neural character based composition models for abuse detection"
    - "towards crafting text adversarial samples"

Connected through hyperedge HE-3 (induced by source paper "automatic testing and improvement of machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "modeling recurrence for transformer"
    - "robust neural machine translation with doubly adversarial inputs"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-4 (induced by source paper "training on synthetic noise improves robustness to natural noise in machine translation"):
  with members:
    - "mtnt a testbed for machine translation of noisy text"
    - "attention is all you need"
    - "robsut wrod reocginiton via semi character recurrent neural network"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "neural machine translation of text from non native speakers"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "grammatical error correction with neural reinforcement learning"
    - "nematus a toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "neural language correction with character based attention"

Connected through hyperedge HE-6 (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"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

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)