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: "evasion and hardening of tree ensemble classifiers"
  Abstract: Classifier evasion consists in finding for a given instance $x$ the nearest instance $x'$ such that the classifier predictions of $x$ and $x'$ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.

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"
    - "robust decision trees against adversarial examples"
    - "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"

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"
    - "robust decision trees against adversarial examples"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "additive tree ensembles reasoning about potential instances"):
  with members:
    - "on validating repairing and refining heuristic ml explanations"
    - "verifying robustness of gradient boosted models"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "intriguing properties of neural networks"
    - "fairness through awareness"
    - "the sat phase transition"

Connected through hyperedge HE-4 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-5 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "theoretically principled trade off between robustness and accuracy"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "ember an open dataset for training static pe malware machine learning models"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "a general framework for adversarial examples with objectives"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "robust decision trees against adversarial examples"
    - "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"

Connected through hyperedge HE-8 (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"
    - "robust decision trees against adversarial examples"
    - "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"

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)