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 "how to cooperate locally to improve global privacy in social networks on amplification of privacy preserving data aggregation"):
  Abstract: In many systems privacy of users depends on the number of participants applying collectively some method to protect their security. Indeed, there are numerous already classic results about revealing aggregated data from a set of users. The conclusion is usually as follows: if you have enough friends to "aggregate" the private data, you can safely reveal your private information. #R##N#Apart from data aggregation, it has been noticed that in a wider context privacy can be often reduced to being hidden in a crowd. Generally, the problems is how to create such crowd. This task may be not easy in some distributed systems, wherein gathering enough "individuals" is hard for practical reasons. #R##N#Such example are social networks (or similar systems), where users have only a limited number of semi trusted contacts and their aim is to reveal some aggregated data in a privacy preserving manner. This may be particularly problematic in the presence of a strong adversary that can additionally corrupt some users. #R##N#We show two methods that allow to significantly amplify privacy with only limited number of local operations and very moderate communication overhead. Except theoretical analysis we show experimental results on topologies of real-life social networks to demonstrate that our methods can significantly amplify privacy of chosen aggregation protocols even facing a massive attack of a powerful adversary. #R##N#We believe however that our results can have much wider applications for improving security of systems based on locally trusted relations.

Includes members:
  Member "practical fault tolerant data aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards extending noiseless privacy dependent data and more practical approach")
  Member "enhancing the robustness of scale free networks":
    also connected through other hyperedges:
      (none)
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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.SI(Social and Information Networks)