human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "profile based privacy for locally private computations"
  Abstract: Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curator. A major barrier, however, towards a wider adoption of this model is that it offers a poor privacy-utility tradeoff. #R##N#In this work, we address this problem by introducing a new variant of local privacy called profile-based privacy. The central idea is that the problem setting comes with a graph G of data generating distributions, whose edges encode sensitive pairs of distributions that should be made indistinguishable. This provides higher utility because unlike local differential privacy, we no longer need to make every pair of private values in the domain indistinguishable, and instead only protect the identity of the underlying distribution. We establish privacy properties of the profile-based privacy definition, such as post-processing invariance and graceful composition. Finally, we provide mechanisms that are private in this framework, and show via simulations that they achieve higher utility than the corresponding local differential privacy mechanisms.

Connected through hyperedge HE-1 (induced by source paper "local obfuscation mechanisms for hiding probability distributions"):
  with members:
    - "local distribution obfuscation via probability coupling"
    - "generalised differential privacy for text document processing"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "on the compositionality of quantitative information flow"
    - "pufferfish privacy mechanisms for correlated data"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-2 (induced by source paper "local distribution obfuscation via probability coupling"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "sok differential privacies"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "renyi differential privacy"
    - "on the compositionality of quantitative information flow"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Question: Please tell me which class the center node 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)