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: "robust decision trees against adversarial examples"
  Abstract: Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-2 (induced by source paper "when are non parametric methods robust"):
  with members:
    - "universal bayes consistency in metric spaces"
    - "adversarially robust submodular maximization under knapsack constraints"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "on the robustness of deep k nearest neighbors"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "adversarially robust generalization requires more data"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "enhancing gradient based attacks with symbolic intervals"
    - "scaling provable adversarial defenses"
    - "adversarial attack on graph structured data"
    - "formal security analysis of neural networks using symbolic intervals"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "cost sensitive robustness against adversarial examples"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-4 (induced by source paper "provably robust boosted decision stumps and trees against adversarial attacks"):
  with members:
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "scaling provable adversarial defenses"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "logit pairing methods can fool gradient based attacks"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-5 (induced by source paper "robustness verification of tree based models"):
  with members:
    - "formal verification of input output mappings of tree ensembles"
    - "formal verification of decision tree ensemble model and detection of its violating input value ranges"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "verifying robustness of gradient boosted models"
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-6 (induced by source paper "toward finding the global optimal of adversarial examples"):
  with members:
    - "sign opt a query efficient hard label adversarial attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "enhancing gradient based attacks with symbolic intervals"
    - "ml loo detecting adversarial examples with feature attribution"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "mixtrain scalable training of formally robust neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-7 (induced by source paper "bosh an efficient meta algorithm for decision based attacks"):
  with members:
    - "sign opt a query efficient hard label adversarial attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "ml loo detecting adversarial examples with feature attribution"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples for non parametric methods attacks defenses and large sample limits"):
  with members:
    - "robustness certificates against adversarial examples for relu networks"
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "formal verification of cnn based perception systems"
    - "improving the generalization of adversarial training with domain adaptation"
    - "provable robustness of relu networks via maximization of linear regions"

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)