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: "wikipedia for smart machines and double deep machine learning"
  Abstract: Very important breakthroughs in data centric deep learning algorithms led to impressive performance in transactional point applications of Artificial Intelligence (AI) such as Face Recognition, or EKG classification. With all due appreciation, however, knowledge blind data only machine learning algorithms have severe limitations for non-transactional AI applications, such as medical diagnosis beyond the EKG results. Such applications require deeper and broader knowledge in their problem solving capabilities, e.g. integrating anatomy and physiology knowledge with EKG results and other patient findings. Following a review and illustrations of such limitations for several real life AI applications, we point at ways to overcome them. The proposed Wikipedia for Smart Machines initiative aims at building repositories of software structures that represent humanity science & technology knowledge in various parts of life; knowledge that we all learn in schools, universities and during our professional life. Target readers for these repositories are smart machines; not human. AI software developers will have these Reusable Knowledge structures readily available, hence, the proposed name ReKopedia. Big Data is by now a mature technology, it is time to focus on Big Knowledge. Some will be derived from data, some will be obtained from mankind gigantic repository of knowledge. Wikipedia for smart machines along with the new Double Deep Learning approach offer a paradigm for integrating datacentric deep learning algorithms with algorithms that leverage deep knowledge, e.g. evidential reasoning and causality reasoning. For illustration, a project is described to produce ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders. Data is important, but knowledge deep, basic, and commonsense is equally important.

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)