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 "price of dependence stochastic submodular maximization with dependent items"):
  Abstract: In this paper, we study the stochastic submodular maximization problem with dependent items subject to packing constraints such as matroid and knapsack constraints. The input of our problem is a finite set of items, and each item is in a particular state from a set of possible states. After picking an item, we are able to observe its state. We assume a monotone and submodular utility function over items and states, and our objective is to select a group of items adaptively so as to maximize the expected utility. Previous studies on stochastic submodular maximization often assume that items' states are independent, however, this assumption may not hold in general. This motivates us to study the stochastic submodular maximization problem with dependent items. We first introduce the concept of \emph{degree of independence} to capture the degree to which one item's state is dependent on others'. Then we propose a non-adaptive policy that approximates the optimal adaptive policy within a factor of $\alpha(1-e^{-\frac{\kappa}{2}+\frac{\kappa}{18m^2}}-\frac{\kappa+2}{3m\kappa})$ where the value of $\alpha$ is depending on the type of constraints, e.g., $\alpha=1$ for matroid constraint, $\kappa$ is the degree of independence, e.g., $\kappa=1$ for independent items, and $m$ is the number of items. We also analyze the adaptivity gap, i.e., the ratio of the values of best adaptive policy and best non-adaptive policy, of our problem with prefix-closed constraints.

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 "adaptivity gaps for stochastic probing submodular and xos functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pandora s box problem with order constraints")
  Member "no time to observe adaptive influence maximization with partial feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a k hop collaborate game model adaptive strategy to maximize total revenue")
  Member "discrete stochastic submodular maximization adaptive vs non adaptive vs offline":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal adaptivity gaps for stochastic multi value probing")
  Member "submodular stochastic probing on matroids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning optimal search algorithms from data")
  Member "submodular function maximization via the multilinear relaxation and contention resolution schemes":
    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 "adaptive submodularity theory and applications in active learning and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")

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)