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: "deceptiveness of internet data for disease surveillance"
  Abstract: Quantifying how many people are or will be sick, and where, is a critical ingredient in reducing the burden of disease because it helps the public health system plan and implement effective outbreak response. This process of disease surveillance is currently based on data gathering using clinical and laboratory methods; this distributed human contact and resulting bureaucratic data aggregation yield expensive procedures that lag real time by weeks or months. The promise of new surveillance approaches using internet data, such as web event logs or social media messages, is to achieve the same goal but faster and cheaper. However, prior work in this area lacks a rigorous model of information flow, making it difficult to assess the reliability of both specific approaches and the body of work as a whole. #R##N#We model disease surveillance as a Shannon communication. This new framework lets any two disease surveillance approaches be compared using a unified vocabulary and conceptual model. Using it, we describe and compare the deficiencies suffered by traditional and internet-based surveillance, introduce a new risk metric called deceptiveness, and offer mitigations for some of these deficiencies. This framework also makes the rich tools of information theory applicable to disease surveillance. This better understanding will improve the decision-making of public health practitioners by helping to leverage internet-based surveillance in a way complementary to the strengths of traditional surveillance.

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.IT(Information Theory)