human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "pots protective optimization technologies":
    also connected through other hyperedges:
      (none)
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep fingerprinting undermining website fingerprinting defenses with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blind adversarial network perturbations")
  Member "cost sensitive robustness against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on training robust pdf malware classifiers")
  Member "on the suitability of l_p norms for creating and preventing adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deep text classification can be fooled":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "lipschitz margin training scalable certification of perturbation invariance for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "varmixup exploiting the latent space for robust training and inference")

Question: Please tell me which class the source paper 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)