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: "robsut wrod reocginiton via semi character recurrent neural network"
  Abstract: Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has demonstrated such a robust word processing mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with little cost. On the other hand, computational models for word recognition (e.g. spelling checkers) perform poorly on data with such noise. Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN). In our experiments, we demonstrate that scRNN has significantly more robust performance in word spelling correction (i.e. word recognition) compared to existing spelling checkers and character-based convolutional neural network. Furthermore, we demonstrate that the model is cognitively plausible by replicating a psycholinguistics experiment about human reading difficulty using our model.

Connected through hyperedge HE-1 (induced by source paper "exploring and improving robustness of multi task deep neural networks via domain agnostic defenses"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adapting sequence models for sentence correction"
    - "stochastic answer networks for machine reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "robustness to programmable string transformations via augmented abstract training"):
  with members:
    - "popqorn quantifying robustness of recurrent neural networks"
    - "combating adversarial misspellings with robust word recognition"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural adversarial examples"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "combating adversarial misspellings with robust word recognition"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spelling error correction using a nested rnn model and pseudo training data"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "improving robustness of machine translation with synthetic noise"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "mtnt a testbed for machine translation of noisy text"
    - "synthetic and natural noise both break neural machine translation"
    - "regularization techniques for fine tuning in neural machine translation"
    - "six challenges for neural machine translation"
    - "domain control for neural machine translation"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-6 (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"
    - "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "neural semi markov conditional random fields for robust character based part of speech tagging"):
  with members:
    - "wronging a right generating better errors to improve grammatical error detection"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "mimicking word embeddings using subword rnns"
    - "a general purpose tagger with convolutional neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural word segmentation learning for chinese"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-8 (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"
    - "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"

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)