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: "re incentivizing discovery mechanisms for partial progress sharing in research"
  Abstract: An essential primitive for an efficient research ecosystem is \emph{partial-progress sharing} (PPS) -- whereby a researcher shares information immediately upon making a breakthrough. This helps prevent duplication of work; however there is evidence that existing reward structures in research discourage partial-progress sharing. Ensuring PPS is especially important for new online collaborative-research platforms, which involve many researchers working on large, multi-stage problems. #R##N#We study the problem of incentivizing information-sharing in research, under a stylized model: non-identical agents work independently on subtasks of a large project, with dependencies between subtasks captured via an acyclic subtask-network. Each subtask carries a reward, given to the first agent who publicly shares its solution. Agents can choose which subtasks to work on, and more importantly, when to reveal solutions to completed subtasks. Under this model, we uncover the strategic rationale behind certain anecdotal phenomena. Moreover, for any acyclic subtask-network, and under a general model of agent-subtask completion times, we give sufficient conditions that ensure PPS is incentive-compatible for all agents. #R##N#One surprising finding is that rewards which are approximately proportional to perceived task-difficulties are sufficient to ensure PPS in all acyclic subtask-networks. The fact that there is no tension between local fairness and global information-sharing in multi-stage projects is encouraging, as it suggests practical mechanisms for real-world settings. Finally, we show that PPS is necessary, and in many cases, sufficient, to ensure a high rate of progress in research.

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)