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: "information gathering in networks via active exploration"
  Abstract: How should we gather information in a network, where each node's visibility is limited to its local neighborhood? This problem arises in numerous real-world applications, such as surveying and task routing in social networks, team formation in collaborative networks and experimental design with dependency constraints. Often the informativeness of a set of nodes can be quantified via a submodular utility function. Existing approaches for submodular optimization, however, require that the set of all nodes that can be selected is known ahead of time, which is often unrealistic. In contrast, we propose a novel model where we start our exploration from an initial node, and new nodes become visible and available for selection only once one of their neighbors has been chosen. We then present a general algorithm NetExp for this problem, and provide theoretical bounds on its performance dependent on structural properties of the underlying network. We evaluate our methodology on various simulated problem instances as well as on data collected from social question answering system deployed within a large enterprise.

Connected through hyperedge HE-1 (induced by source paper "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"

Connected through hyperedge HE-2 (induced by source paper "information gathering with peers submodular optimization with peer prediction constraints"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "a framework for designing information elicitation mechanisms that reward truth telling"
    - "learning to incentivize eliciting effort via output agreement"
    - "crowd access path optimization diversity matters"
    - "incentives for privacy tradeoff in community sensing"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "a utility framework for bounded loss market makers"

Connected through hyperedge HE-3 (induced by source paper "online influence maximization under independent cascade model with semi bandit feedback"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "model independent online learning for influence maximization"
    - "cascading bandits for large scale recommendation problems"
    - "cascading bandits learning to rank in the cascade model"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "an adaptive algorithm for finite stochastic partial monitoring"

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.AI(Artificial Intelligence)