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: "efficient independence based map approach for robust markov networks structure discovery"
  Abstract: This work introduces the IB-score, a family of independence-based score functions for robust learning of Markov networks independence structures. Markov networks are a widely used graphical representation of probability distributions, with many applications in several fields of science. The main advantage of the IB-score is the possibility of computing it without the need of estimation of the numerical parameters, an NP-hard problem, usually solved through an approximate, data-intensive, iterative optimization. We derive a formal expression for the IB-score from first principles, mainly maximum a posteriori and conditional independence properties, and exemplify several instantiations of it, resulting in two novel algorithms for structure learning: IBMAP-HC and IBMAP-TS. Experimental results over both artificial and real world data show these algorithms achieve important error reductions in the learnt structures when compared with the state-of-the-art independencebased structure learning algorithm GSMN, achieving increments of more than 50% in the amount of independencies they encode correctly, and in some cases, learning correctly over 90% of the edges that GSMN learnt incorrectly. Theoretical analysis shows IBMAP-HC proceeds efficiently, achieving these improvements in a time polynomial to the number of random variables in the domain.

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)