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: "maximizing acceptance probability for active friending in on line social networks"
  Abstract: Friending recommendation has successfully contributed to the explosive growth of on-line social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target, has not been explored in existing on-line social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, \emph{Acceptance Probability Maximization (APM)}, and develop a polynomial time algorithm, called \emph{Selective Invitation with Tree and In-Node Aggregation (SITINA)}, to find the optimal solution. We implement an active friending service with SITINA in Facebook to validate our idea. Our user study and experimental results manifest that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.

Connected through hyperedge HE-1 (induced by source paper "an approximation algorithm for active friending in online social networks"):
  with members:
    - "beyond uniform reverse sampling a hybrid sampling technique for misinformation prevention"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "the densest k subhypergraph problem"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-2 (induced by source paper "boosting information spread an algorithmic approach"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "the simple rules of social contagion"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "social dynamics of digg"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "locally adaptive optimization adaptive seeding for monotone submodular functions"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "maximizing social influence in nearly optimal time"
    - "symmetry and approximability of submodular maximization problems"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "inferring networks of diffusion and influence"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "combining traditional marketing and viral marketing with amphibious influence maximization"):
  with members:
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"

Connected through hyperedge HE-5 (induced by source paper "the routing of complex contagion in kleinberg s small world networks"):
  with members:
    - "complex contagions in kleinberg s small world model"

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)