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: "optimal investment strategies for competing camps in a social network a broad framework"
  Abstract: We study the problem of optimally investing in nodes of a social network in a competitive setting, wherein two camps aim to drive the average opinion of the population in their own favor. Using a well-established model of opinion dynamics, we formulate the problem as a zero-sum game with its players being the two camps. We derive optimal investment strategies for both camps, and show that a random investment strategy is optimal when the underlying network follows a popular class of weight distributions. We study a broad framework, where we consider various well-motivated settings of the problem, namely, when the influence of a camp on a node is a concave function of its investment on that node, when a camp aims at maximizing competitor's investment or deviation from its desired investment, and when one of the camps has uncertain information about the values of the model parameters. We also study a Stackelberg variant of this game under common coupled constraints on the combined investments by the camps and derive their equilibrium strategies, and hence quantify the first-mover advantage. For a quantitative and illustrative study, we conduct simulations on real-world datasets and provide results and insights.

Connected through hyperedge HE-1 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks"):
  with members:
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "polarization and fake news early warning of potential misinformation targets"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "spread of information with confirmation bias in cyber social networks"
    - "fake news in social networks"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "network inference from consensus dynamics"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-2 (induced by source paper "competitive information spread with confirmation bias in cyber social networks"):
  with members:
    - "spread of information with confirmation bias in cyber social networks"
    - "structural balance and opinion separation in trust mistrust social networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"

Connected through hyperedge HE-3 (induced by source paper "a two phase investment game for competitive opinion dynamics in social networks"):
  with members:
    - "an integrated framework for competitive multi channel marketing of multi featured products"
    - "multi round influence maximization"
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "optimal multiphase investment strategies for influencing opinions in a social network"
    - "adaptive influence maximization in dynamic social networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "information diffusion in social networks in two phases"
    - "no time to observe adaptive influence maximization with partial feedback"

Connected through hyperedge HE-4 (induced by source paper "optimal multiphase investment strategies for influencing opinions in a social network"):
  with members:
    - "effectiveness of diffusing information through a social network in multiple phases"
    - "adaptive influence maximization in dynamic social networks"
    - "strategic disclosure of opinions on a social network"
    - "information diffusion in social networks in two phases"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "a multi phase approach for improving information diffusion in social networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

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)