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 "seeding with costly network information"):
  Abstract: The spread of behavior over social networks depends on the contact structure among individuals, and seeding the most influential agents can substantially enhance the extent of the spread. While the choice of the best seed set, known as influence maximization, is a computationally hard problem, many computationally efficient algorithms have been proposed to approximate the optimal seed sets with provable guarantees. Most of the previous work on influence maximization assumes the knowledge of the entire network graph. However, in practice, obtaining full knowledge of the network structure is very costly. #R##N#In this work, we consider the choice of $k$ initial seeds to maximize the expected number of adopters under the independent cascade model. We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using $\tilde{O}({p} n^2 + \sqrt{p} n^{1.5})$ edge queries in $\tilde{O}({p} n^2 + \sqrt{p} n^{1.5})$ time, where $n$ is the network size and $p$ is the probability of spreading through an edge. To the best of our knowledge, this is the first result to provide approximation guarantees for influence maximization, using a sub-quadratic number of queries for polynomially small $p$. We complement this result by showing that it is impossible to approximate the problem using $o(n^2)$ edge queries for constant $p$. In the end, we consider a more advanced query model, where one seeds a node and observes the resultant adopters after running the spreading process. We provide an algorithm that uses only $\tilde O(k^2)$ such queries and provides almost tight approximation guarantees.

Includes members:
  Member "beyond worst case in approximability of nonsubmodular influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")
  Member "metric sublinear algorithms via linear sampling":
    also connected through other hyperedges:
      (none)
  Member "the importance of communities for learning to influence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization")
  Member "stochastic submodular maximization the case of coverage functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end learning and optimization on graphs")
  Member "online influence maximization under independent cascade model with semi bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable influence estimation without sampling")
  Member "almost optimal streaming algorithms for coverage problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "online influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic ensemble learning for online influence maximization")

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)