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 "on differentially private online collaborative recommendation systems"):
  Abstract: In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy in terms of the standard differential privacy. We give the first quantitative analysis of the trade-offs between recommendation quality and users' privacy in such a system by showing a lower bound on the best achievable privacy for any non-trivial algorithm, and proposing a near-optimal algorithm. From our results, we find that there is actually little trade-off between recommendation quality and privacy for any non-trivial algorithm. Our results also identify the key parameters that determine the best achievable privacy.

Includes members:
  Member "private decayed predicate sums on streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss")
  Member "beating randomized response on incoherent matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "beyond worst case analysis in private singular vector computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pd ml lite private distributed machine learning from lighweight cryptography")
  Member "differentially private online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating differentially private machine learning in practice")
  Member "a learning theory approach to non interactive database privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "interactive privacy via the median mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")

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)