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: "improving robustness of ml classifiers against realizable evasion attacks using conserved features"
  Abstract: Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, ML models are often susceptible to evasion attacks, in which an adversary makes changes to the input (such as malware) in order to avoid being detected. A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change. We investigate the effectiveness of this approach to designing robust ML in the face of attacks that can be realized in actual malware (realizable attacks). We demonstrate that in the context of structure-based PDF malware detection, such techniques appear to have limited effectiveness, but they are effective with content-based detectors. In either case, we show that augmenting the feature space models with conserved features (those that cannot be unilaterally modified without compromising malicious functionality) significantly improves performance. Finally, we show that feature space models enable generalized robustness when faced with a variety of realizable attacks, as compared to classifiers which are tuned to be robust to a specific realizable attack.

Connected through hyperedge HE-1 (induced by source paper "provable robust learning based on transformation specific smoothing"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-2 (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"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "humans can decipher adversarial images"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-4 (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-5 (induced by source paper "defending against physically realizable attacks on image classification"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "scaling provable adversarial defenses"
    - "sentinet detecting physical attacks against deep learning systems"
    - "curriculum adversarial training"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient formal safety analysis of 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)