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 "towards plausible graph anonymization"):
  Abstract: Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs contain sensitive information, they need to be properly anonymized before release. Most of the existing graph anonymization mechanisms rely on the perturbation of the original graph's edge set. In this paper, we identify a fundamental weakness of these mechanisms: They neglect the strong structural proximity between friends in social graphs, thus add implausible fake edges for anonymization.  To exploit this weakness, we first propose a metric to quantify an edge's plausibility by relying on graph embedding. Extensive experiments on three real-life social network datasets demonstrate that our plausibility metric can very effectively differentiate fake edges from original edges with AUC (area under the ROC curve) values above 0.95 in most of the cases. We then rely on a Gaussian mixture model to automatically derive the threshold on the edge plausibility values to determine whether an edge is fake, which enables us to recover to a large extent the original graph from the anonymized graph. We further demonstrate that our graph recovery attack jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms.  To mitigate this vulnerability, we propose a method to generate fake yet plausible edges given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms decrease the chances of graph recovery, reduce the success of graph de-anonymization (up to 30%), and provide even better utility than the existing anonymization mechanisms.

Includes members:
  Member "memguard defending against black box membership inference attacks via adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "language in our time an empirical analysis of hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "tagvisor a privacy advisor for sharing hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "privacy in social media identification mitigation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "ml leaks model and data independent membership inference attacks and defenses on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "knock knock who s there membership inference on aggregate location data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "walk2friends inferring social links from mobility profiles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "evolution of ego networks in social media with link recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tubes bubbles topological confinement of youtube recommendations")

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.CR(Cryptography and Security)