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: "evading classifiers in discrete domains with provable optimality guarantees"
  Abstract: Security-critical applications such as malware, fraud, or spam detection, require machine learning models that operate on examples from constrained discrete domains. In these settings, gradient-based attacks that rely on adding perturbations often fail to produce adversarial examples that meet the domain constraints, and thus are not effective. We introduce a graphical framework that (1) formalizes existing attacks in discrete domains, (2) efficiently produces valid adversarial examples with guarantees of minimal cost, and (3) can accommodate complex cost functions beyond the commonly used p-norm. We demonstrate the effectiveness of this method by crafting adversarial examples that evade a Twitter bot detection classifier using a provably minimal number of changes.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "intriguing properties of adversarial ml attacks in the problem space"):
  with members:
    - "misleading authorship attribution of source code using adversarial learning"
    - "on evaluating adversarial robustness"
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "textbugger generating adversarial text against real world applications"
    - "generating natural language adversarial examples"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "explaining black box android malware detection"

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)