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: "hardening classifiers against evasion the good the bad and the ugly"
  Abstract: Machine learning is widely used in security applications, particularly in the form of statistical classification aimed at distinguishing benign from malicious entities. Recent research has shown that such classifiers are often vulnerable to evasion attacks, whereby adversaries change behavior to be categorized as benign while preserving malicious functionality. Research into evasion attacks has followed two paradigms: attacks in problem space, where the actual malicious instance such as the PDF file, is modified, and attacks in feature space, where the evasion attack is abstracted into directly modifying numerical features corresponding to malicious instances rather than instances themselves. However, there exists no prior validation of the effectiveness of feature space threat models in representing real evasion attacks. We make several contributions to address this gap, using PDF malware detection as a case study, with four PDF malware detectors. First, we use iterative retraining to create a baseline for evasion-robust PDF malware detection by using an automated problem space attack generator in the retraining loop. Second, we use this baseline to demonstrate that replacing problem space attacks with feature space attacks may significantly reduce the robustness of the resulting classifier. Third, we demonstrate the existence of conserved (or invariant) features, show how these can be leveraged to design evasion-robust classifiers that are nearly as effective as those relying on the problem space attack, and present an approach for automatically identifying conserved features of PDF malware detectors. Finally, we evaluate generalizability of evasion defense through retraining by considering two additional evasion attacks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "better safe than sorry an adversarial approach to improve social bot detection"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "spectrum based deep neural networks for fraud detection"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "dna inspired online behavioral modeling and its application to spambot detection"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-3 (induced by source paper "the naked sun malicious cooperation between benign looking processes"):
  with members:
    - "poisoning behavioral malware clustering"
    - "black box attacks against rnn based malware detection algorithms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial detection of flash malware limitations and open issues"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-4 (induced by source paper "characterizing adversarial examples based on spatial consistency information for semantic segmentation"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "generating adversarial examples with adversarial networks"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "deep layer aggregation"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "blocking transferability of adversarial examples in black box learning systems"

Connected through hyperedge HE-5 (induced by source paper "adversarial malware binaries evading deep learning for malware detection in executables"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "adversarial detection of flash malware limitations and open issues"
    - "language modeling with gated convolutional networks"
    - "the mythos of model interpretability"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-6 (induced by source paper "attack and defense of dynamic analysis based adversarial neural malware classification models"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "ensemble robustness of deep learning algorithms"
    - "stealing machine learning models via prediction apis"
    - "practical black box attacks against deep learning systems using adversarial examples"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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.CR(Cryptography and Security)