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: "structural analysis of criminal network and predicting hidden links using machine learning"
  Abstract: Analysis of criminal networks is inherently difficult because of the nature of the topic. Criminal networks are covert and most of the information is not publicly available. This leads to small datasets available for analysis. The available criminal network datasets consists of entities, i.e. individual or organizations, which are linked to each other. The links between entities indicates that there is a connection between these entities such as involvement in the same criminal event, having commercial ties, and/or memberships in the same criminal organization. Because of incognito criminal activities, there could be many hidden links from entities to entities, which makes the publicly available criminal networks incomplete. Revealing hidden links introduces new information, e.g. affiliation of a suspected individual with a criminal organization, which may not be known with public information. What will we be able to find if we can run analysis on a larger dataset and use link prediction to reveal the implicit connections? We plan to answer this question by using a dataset that is an order of magnitude more than what is used in most criminal networks analysis. And by using machine learning techniques, we will convert a link prediction problem to a binary classification problem. We plan to reveal hidden links and potentially hidden key attributes of the criminal network. With a more complete picture of the network, we can potentially use this data to thwart criminal organizations and/or take a Pareto approach in targeting key nodes. We conclude our analysis with an effective destruction strategy to weaken criminal networks and prove the effectiveness of revealing hidden links when attacking to criminal networks.

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.SI(Social and Information Networks)