human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adaptive influence maximization with myopic feedback"):
  Abstract: We study the adaptive influence maximization problem with myopic feedback under the independent cascade model: one sequentially selects k nodes as seeds one by one from a social network, and each selected seed returns the immediate neighbors it activates as the feedback available for later selections, and the goal is to maximize the expected number of total activated nodes, referred as the influence spread. We show that the adaptivity gap, the ratio between the optimal adaptive influence spread and the optimal non-adaptive influence spread, is at most 4 and at least e/(e-1), and the approximation ratios with respect to the optimal adaptive influence spread of both the non-adaptive greedy and adaptive greedy algorithms are at least \frac{1}{4}(1 - \frac{1}{e}) and at most \frac{e^2 + 1}{(e + 1)^2} < 1 - \frac{1}{e}. Moreover, the approximation ratio of the non-adaptive greedy algorithm is no worse than that of the adaptive greedy algorithm, when considering all graphs. Our result confirms a long-standing open conjecture of Golovin and Krause (2011) on the constant approximation ratio of adaptive greedy with myopic feedback, and it also suggests that adaptive greedy may not bring much benefit under myopic feedback.

Includes members:
  Member "near optimal adaptivity gaps for stochastic multi value probing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pandora s box problem with order constraints")
  Member "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time constrained adaptive influence maximization")
  Member "adaptive influence maximization under general feedback models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time constrained adaptive influence maximization")
  Member "multi round influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time constrained adaptive influence maximization")
  Member "adaptive submodular influence maximization with myopic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time constrained adaptive influence maximization")
  Member "adaptive influence maximization in dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time constrained adaptive influence maximization")
  Member "adaptivity gaps for stochastic probing submodular and xos functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pandora s box problem with order constraints")
  Member "locally adaptive optimization adaptive seeding for monotone submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient approximation algorithms for adaptive seed minimization")

Question: Please tell me which class the source paper 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)