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 "ctrl z recovering anonymized social graphs"):
  Abstract: Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, due to the sensitivity of information contained in such social graphs, they need to be properly anonymized before release. Most of the graph anonymization techniques that have been proposed to sanitize social graph data rely on the perturbation of the original graph's structure, more specifically of its edge set. In this paper, we identify a fundamental weakness of these edge-based anonymization mechanisms and exploit it to recover most of the original graph structure. #R##N#First, we propose a method to quantify an edge's plausibility in a given graph by relying on graph embedding. Our experiments on three real-life social network datasets under two widely known graph anonymization mechanisms demonstrate that this method can very effectively detect fake edges with AUC values above 0.95 in most cases. Second, by relying on Gaussian mixture models and maximum a posteriori probability estimation, we derive an optimal decision rule to detect whether an edge is fake based on the observed graph data. We further demonstrate that this approach concretely jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms. To mitigate this vulnerability, we propose a method to generate fake edges as plausible as possible given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms not only decrease the chances of graph recovery (with AUC dropping by up to 35%), but also provide even better graph utility than existing anonymization methods.

Includes members:
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep graph matching consensus")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "preserving link privacy in social network based systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "target privacy preserving for social networks")
  Member "de anonymizing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")

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)