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: "the optimal mechanism in differential privacy multidimensional setting"
  Abstract: We derive the optimal $\epsilon$-differentially private mechanism for a general two-dimensional real-valued (histogram-like) query function under a utility-maximization (or cost-minimization) framework for the $\ell^1$ cost function. We show that the optimal noise probability distribution has a correlated multidimensional staircase-shaped probability density function. Compared with the Laplacian mechanism, we show that in the high privacy regime (as $\epsilon \to 0$), the Laplacian mechanism is approximately optimal; and in the low privacy regime (as $\epsilon \to +\infty$), the optimal cost is $\Theta(e^{-\frac{\epsilon}{3}})$, while the cost of the Laplacian mechanism is $\frac{2\Delta}{\epsilon}$, where $\Delta$ is the sensitivity of the query function. We conclude that the gain is more pronounced in the low privacy regime. We conjecture that the optimality of the staircase mechanism holds for vector-valued (histogram-like) query functions with arbitrary dimension, and holds for many other classes of cost functions as well.

Connected through hyperedge HE-1 (induced by source paper "extremal mechanisms for local differential privacy"):
  with members:
    - "differentially private multi party computation optimality of non interactive randomized response"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "the optimal mechanism in differential privacy"
    - "convergence rates for differentially private statistical estimation"

Connected through hyperedge HE-2 (induced by source paper "differentially private multi party computation optimality of non interactive randomized response"):
  with members:
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "the optimal mechanism in differential privacy"
    - "beating randomized response on incoherent matrices"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-3 (induced by source paper "the optimal mechanism in differential privacy"):
  with members:
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "impossibility of differentially private universally optimal mechanisms"
    - "universally optimal privacy mechanisms for minimax agents"
    - "on the geometry of differential privacy"
    - "universally utility maximizing privacy mechanisms"

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)