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 "the optimal differential privacy mechanism under hamming distortion for universal memoryless source classes"):
  Abstract: To be considered for the 2016 IEEE Jack Keil Wolf ISIT Student Paper Award. We develop the tradeoff between privacy, quantified using local differential privacy (L-DP), and utility, quantified using Hamming distortion, for specific classes of universal memoryless finite-alphabet sources. In particular, for the class of permutation invariant sources (i.e., sources whose distributions are invariant under permutations), the optimal L-DP mechanism is obtained. On the other hand, for the class of sources with ordered statistics (i.e., for every distribution $P=(P_1,P_2,...,P_M) \in \mathcal{P}, P_1 \ge P_2 \ge P_3 \ge \ldots \ge P_M$), upper and lower bounds on the achievable local differential privacy are derived with optimality results for specific range of distortions.

Includes members:
  Member "differential privacy as a mutual information constraint":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "information extraction under privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "on the relation between identifiability differential privacy and mutual information privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "extremal mechanisms for local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "optimal noise adding mechanisms for approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reviewing and improving the gaussian mechanism for differential privacy")
  Member "utility privacy tradeoffs in databases an information theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "privacy against statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "on the relation between differential privacy and quantitative information flow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "information theoretic metrics for local differential privacy protocols")

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.IT(Information Theory)