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: "achieving verified robustness to symbol substitutions via interval bound propagation"
  Abstract: Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and data augmentation to partially mitigate such brittleness, but these are unlikely to find worst-case adversaries due to the complexity of the search space arising from discrete text perturbations. In this work, we approach the problem from the opposite direction: to formally verify a system's robustness against a predefined class of adversarial attacks. We study text classification under synonym replacements or character flip perturbations. We propose modeling these input perturbations as a simplex and then using Interval Bound Propagation -- a formal model verification method. We modify the conventional log-likelihood training objective to train models that can be efficiently verified, which would otherwise come with exponential search complexity. The resulting models show only little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries.

Connected through hyperedge HE-1 (induced by source paper "robustness verification for transformers"):
  with members:
    - "correctness verification of neural networks"
    - "adversarial examples with difficult common words for paraphrase identification"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "verification of non linear specifications for neural networks"
    - "visualbert a simple and performant baseline for vision and language"

Connected through hyperedge HE-2 (induced by source paper "automatic perturbation analysis on general computational graphs"):
  with members:
    - "robustness verification for transformers"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "fastened crown tightened neural network robustness certificates"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "popqorn quantifying robustness of recurrent neural networks"
    - "asymmetric valleys beyond sharp and flat local minima"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "reducing sentiment bias in language models via counterfactual evaluation"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "certified robustness to adversarial word substitutions"
    - "release strategies and the social impacts of language models"
    - "multi task deep neural networks for natural language understanding"
    - "reducing gender bias in word level language models with a gender equalizing loss function"
    - "transformer xl attentive language models beyond a fixed length context"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-5 (induced by source paper "tight certificates of adversarial robustness for randomly smoothed classifiers"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness to adversarial word substitutions"
    - "the logbarrier adversarial attack making effective use of decision boundary information"
    - "towards robust locally linear deep networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-6 (induced by source paper "advcodec towards a unified framework for adversarial text generation"):
  with members:
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)