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: "using node centrality and optimal control to maximize information diffusion in social networks"
  Abstract: We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function—implemented through advertisements—during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin’s maximum principle from optimal control theory and solve it numerically using the forward–backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness, and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree—a simple and local measure—performs well on the three social networks used to demonstrate results: 1) scientific collaboration; 2) Slashdot; and 3) Facebook. The optimal strategy targets central nodes when the resource is scarce, but noncentral nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models—that can be modeled using a system of ordinary differential equations—for a network with a known adjacency matrix.

Connected through hyperedge HE-1 (induced by source paper "publishing community preserving attributed social graphs with a differential privacy guarantee"):
  with members:
    - "robust active attacks on social graphs"
    - "community detection through likelihood optimization in search of a sound model"
    - "detecting communities under differential privacy"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "a scalable generative graph model with community structure"
    - "community detection in networks with node attributes"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "preserving link privacy in social network based systems"

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)