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: "on optimal approximability results for computing the strong metric dimension"
  Abstract: The strong metric dimension of a graph was first introduced by Seb and Tannier (2004) as an alternative to the (weak) metric dimension of graphs previously introduced independently by Slater (1975) and by Harary and Melter (1976), and has since been investigated in several research papers. However, the exact worst-case computational complexity of computing the strong metric dimension has remained open beyond being NP-complete. In this communication, we show that the problem of computing the strong metric dimension of a graph of n nodes admits a polynomial-time 2-approximation, admits a O(20.287n)-time exact computation algorithm, admits a O(1.2738k+nk)-time exact computation algorithm if the strong metric dimension is at most k, does not admit a polynomial time (2)-approximation algorithm assuming the unique games conjecture is true, does not admit a polynomial time (10521)-approximation algorithm assuming PNP, does not admit a O(2o(n))-time exact computation algorithm assuming the exponential time hypothesis is true, and does not admit a O(no(k))-time exact computation algorithm if the strong metric dimension is at most k assuming the exponential time hypothesis is true.

Connected through hyperedge HE-1 (induced by source paper "on analyzing and evaluating privacy measures for social networks under active attack"):
  with members:
    - "privacy preserving machine learning through data obfuscation"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "motifs in temporal networks"
    - "on the computational complexities of three privacy measures for large networks under active attack"
    - "computing the diameter polynomially faster than apsp"

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.CC(Computational Complexity)