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: "quickest detection with social learning interaction of local and global decision makers"
  Abstract: We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, that is, each agent records a private observation of a noisy underlying state process, selfishly optimizes its local utility and then broadcasts its local decision. Given these local decisions, how can a global decision maker achieve quickest time change detection when the underlying state changes according to a phase-type distribution? The paper presents four results. First, using Blackwell dominance of measures, it is shown that the optimal cost incurred in social learning based quickest detection is always larger than that of classical quickest detection. Second, it is shown that in general the optimal decision policy for social learning based quickest detection is characterized by multiple thresholds within the space of Bayesian distributions. Third, using lattice programming and stochastic dominance, sufficient conditions are given for the optimal decision policy to consist of a single linear hyperplane, or, more generally, a threshold curve. Estimation of the optimal linear approximation to this threshold curve is formulated as a simulation-based stochastic optimization problem. Finally, the paper shows that in multi-agent sensor management with quickest detection, where each agent views the world according to its prior, the optimal policy has a similar structure to social learning.

Connected through hyperedge HE-1 (induced by source paper "rate of learning in hierarchical social networks"):
  with members:
    - "detection performance in balanced binary relay trees with node and link failures"
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "subexponential convergence for information aggregation on regular trees"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-2 (induced by source paper "social teaching being informative vs being right in sequential decision making"):
  with members:
    - "keep ballots secret on the futility of social learning in decision making by voting"
    - "quantization of prior probabilities for collaborative distributed hypothesis testing"
    - "quantization of prior probabilities for hypothesis testing"

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.GT(Computer Science and Game Theory)