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: "towards the design of prospect theory based human decision rules for hypothesis testing"
  Abstract: Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing rules for behavioral agents, whose cognitive behavior is not captured by traditional utility functions such as Bayes risk. In this paper, we adopt prospect theory based models for decision makers. We consider special agent models namely optimists and pessimists in this paper, and derive optimal detection rules under different scenarios. Using an illustrative example, we also show how the decision rule of a human agent deviates from the Bayesian decision rule under various behavioral models, considered in this paper.

Connected through hyperedge HE-1 (induced by source paper "beliefs and expertise in sequential decision making"):
  with members:
    - "strategic communication between prospect theoretic agents over a gaussian test channel"
    - "distributed hypothesis testing with social learning and symmetric fusion"
    - "social teaching being informative vs being right in sequential decision making"
    - "quantization of prior probabilities for collaborative distributed hypothesis testing"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of likelihood ratio test for prospect theory based binary hypothesis testing"):
  with members:
    - "strategic communication between prospect theoretic agents over a gaussian test channel"

Connected through hyperedge HE-3 (induced by source paper "strategic communication between prospect theoretic agents over a gaussian test channel"):
  with members:
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "spectrum investment under uncertainty a behavioral economics perspective"

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.AI(Artificial Intelligence)