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: "minimizing seed set selection with probabilistic coverage guarantee in a social network"
  Abstract: A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum size so that with a guaranteed probability the number of users discussing the topic would reach a given threshold. We formulate the task as an optimization problem called seed minimization with probabilistic coverage guarantee (SM-PCG). This problem departs from the previous studies on social influence maximization or seed minimization because it considers influence coverage with probabilistic guarantees instead of guarantees on expected influence coverage. We show that the problem is not submodular, and thus is harder than previously studied problems based on submodular function optimization. We provide an approximation algorithm and show that it approximates the optimal solution with both a multiplicative ratio and an additive error. The multiplicative ratio is tight while the additive error would be small if influence coverage distributions of certain seed sets are well concentrated. For one-way bipartite graphs we analytically prove the concentration condition and obtain an approximation algorithm with an $O(\log n)$ multiplicative ratio and an $O(\sqrt{n})$ additive error, where $n$ is the total number of nodes in the social graph. Moreover, we empirically verify the concentration condition in real-world networks and experimentally demonstrate the effectiveness of our proposed algorithm comparing to commonly adopted benchmark algorithms.

Connected through hyperedge HE-1 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "imrank influence maximization via finding self consistent ranking"

Connected through hyperedge HE-2 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "robust influence maximization"
    - "robust influence maximization"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-3 (induced by source paper "cost effective seed selection in online social networks"):
  with members:
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "boosting information spread an algorithmic approach"
    - "least cost influence maximization across multiple social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-4 (induced by source paper "popular topics spread faster new dimension for influence propagation in online social networks"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "influence maximization in continuous time diffusion networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive influence maximization in dynamic social networks"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "influence maximization in continuous time diffusion networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "cumulative activation in social networks"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "shaping social activity by incentivizing users"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

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.SI(Social and Information Networks)