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: "greedy attack and gumbel attack generating adversarial examples for discrete data"
  Abstract: We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples with difficult common words for paraphrase identification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "knowing when to stop evaluation and verification of conformity to output size specifications"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "motivating the rules of the game for adversarial example research"
    - "foil it find one mismatch between image and language caption"
    - "maximum resilience of artificial neural networks"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-3 (induced by source paper "natural adversarial sentence generation with gradient based perturbation"):
  with members:
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "universal language model fine tuning for text classification"
    - "towards crafting text adversarial samples"
    - "hotflip white box adversarial examples for text classification"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "synthetic and natural noise both break neural machine translation"
    - "simple black box adversarial perturbations for deep networks"

Connected through hyperedge HE-4 (induced by source paper "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"):
  with members:
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "adversarial texts with gradient methods"
    - "towards crafting text adversarial samples"
    - "learning universal adversarial perturbations with generative models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "on the robustness of semantic segmentation models to adversarial attacks"

Connected through hyperedge HE-5 (induced by source paper "adversarial reprogramming of text classification neural networks"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "adversarial patch"
    - "adversarial reprogramming of neural networks"
    - "black box attacks against rnn based malware detection algorithms"
    - "neural machine translation with gumbel greedy decoding"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-6 (induced by source paper "learning to explain an information theoretic perspective on model interpretation"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "learning important features through propagating activation differences"
    - "the mythos of model interpretability"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "striving for simplicity the all convolutional net"
    - "convolutional neural networks for sentence classification"

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.LG(Machine Learning)