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 "chorus differential privacy via query rewriting"):
  Abstract: We present Chorus, a system with a novel architecture for providing differential privacy for statistical SQL queries. The key to our approach is to embed a differential privacy mechanism into the query before execution so the query automatically enforces differential privacy on its output. Chorus is compatible with any SQL database that supports standard math functions, requires no user modifications to the database or queries, and simultaneously supports many differential privacy mechanisms. To the best of our knowledge, no existing system provides these capabilities. #R##N#We demonstrate our approach using four general-purpose differential privacy mechanisms. In the first evaluation of its kind, we use Chorus to evaluate these four mechanisms on real-world queries and data. The results demonstrate that our approach supports 93.9% of statistical queries in our corpus, integrates with a production DBMS without any modifications, and scales to hundreds of millions of records. #R##N#Chorus is currently being deployed at Uber for its internal analytics tasks. Chorus represents a significant part of the company's GDPR compliance efforts, and can provide both differential privacy and access control enforcement. In this capacity, Chorus processes more than 10,000 queries per day.

Includes members:
  Member "differential privacy for growing databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporally discounted differential privacy for evolving datasets on an infinite horizon")
  Member "towards practical differential privacy for sql queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale")
  Member "principled evaluation of differentially private algorithms using dpbench":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "a data and workload aware algorithm for range queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "differentially private data analysis of social networks via restricted sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private identity testing for high dimensional distributions")
  Member "dpcube differentially private histogram release through multidimensional partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mvg mechanism differential privacy under matrix valued query")
  Member "differentially private grids for geospatial data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentially private k means clustering with guaranteed convergence")

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)