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: "sim to real optimization of complex real world mobile network with imperfect information via deep reinforcement learning from self play"
  Abstract: Mobile network that millions of people use every day is one of the most complex systems in real world. Optimization of mobile network to meet exploding customer demand and reduce CAPEX/OPEX poses greater challenges than in prior works. Learning to solve complex problems in real world to benefit everyone and make the world better has long been ultimate goal of AI. However, it still remains an unsolved problem for deep reinforcement learning (DRL), given imperfect information in real world, huge state/action space, lots of data needed for training, associated time/cost, multi-agent interactions, potential negative impact to real world, etc. To bridge this reality gap, we proposed a DRL framework to direct transfer optimal policy learned from multi-tasks in source domain to unseen similar tasks in target domain without any further training in both domains. First, we distilled temporal-spatial relationships between cells and mobile users to scalable 3D image-like tensor to best characterize partially observed mobile network. Second, inspired by AlphaGo, we used a novel self-play mechanism to empower DRL agent to gradually improve its intelligence by competing for best record on multiple tasks. Third, a decentralized DRL method is proposed to coordinate multi-agents to compete and cooperate as a team to maximize global reward and minimize potential negative impact. Using 7693 unseen test tasks over 160 unseen simulated mobile networks and 6 field trials over 4 commercial mobile networks in real world, we demonstrated the capability of our approach to direct transfer the learning from one simulator to another simulator, and from simulation to real world. This is the first time that a DRL agent successfully transfers its learning directly from simulation to very complex real world problems with incomplete and imperfect information, huge state/action space and multi-agent interactions.

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)