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: "targeting interventions in networks"
  Abstract: Individuals interact strategically with their network neighbors. A planner can shape incentives in pursuit of an aggregate goal, such as maximizing welfare or minimizing volatility. We analyze a variety of targeting problems by identifying how a given profile of incentive changes is amplified or attenuated by the strategic spillovers in the network. The optimal policies are simplest when the budget for intervention is large. If actions are strategic complements, the optimal intervention changes all agents' incentives in the same direction and does so in proportion to their eigenvector centralities. In games of strategic substitutes, the optimal intervention is very different: it moves neighbors' incentives in opposite directions, dividing local communities into positively and negatively targeted agents, with few links across these two categories. To derive these results and characterize optimal interventions more generally, we introduce a method of decomposing any potential intervention into principal components determined by the network. A particular ordering of principal components describes the planner's priorities across a range of network intervention problems.

Connected through hyperedge HE-1 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games"):
  with members:
    - "learning quadratic games on networks"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "from behavior to sparse graphical games efficient recovery of equilibria"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "on the complexity of nash equilibria of action graph games"

Connected through hyperedge HE-2 (induced by source paper "learning quadratic games on networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "learning sparse potential games in polynomial time and sample complexity"
    - "active sensing of social networks"
    - "learning laplacian matrix in smooth graph signal representations"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-3 (induced by source paper "contextual centrality going beyond network structures"):
  with members:
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "the groupon effect on yelp ratings a root cause analysis"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-4 (induced by source paper "graphon games a statistical framework for network games and interventions"):
  with members:
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "distributions of centrality on 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.GT(Computer Science and Game Theory)