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: "detecting egregious responses in neural sequence to sequence models"
  Abstract: In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.

Connected through hyperedge HE-1 (induced by source paper "nattack adversarial attacks to bypass a gan based classifier trained to detect network intrusion"):
  with members:
    - "adversarial attacks and defenses in images graphs and text a review"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-3 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-4 (induced by source paper "universal adversarial triggers for attacking and analyzing nlp"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "adversarial patch"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-5 (induced by source paper "say what i want towards the dark side of neural dialogue models"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "metamorphic relation based adversarial attacks on differentiable neural computer"
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "neural approaches to conversational ai"
    - "interpretable adversarial perturbation in input embedding space for text"

Connected through hyperedge HE-6 (induced by source paper "tigs an inference algorithm for text infilling with gradient search"):
  with members:
    - "text infilling"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi modal chinese poetry generation model"
    - "bidirectional beam search forward backward inference in neural sequence models for fill in the blank image captioning"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-7 (induced by source paper "negative training for neural dialogue response generation"):
  with members:
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "towards crafting text adversarial samples"
    - "adversarial neural machine translation"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "adversarial learning for neural dialogue generation"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "exploring the robustness of nmt systems to nonsensical inputs"):
  with members:
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "attention is all you need"
    - "hotflip white box adversarial examples for text 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.AI(Artificial Intelligence)