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: "balancing speed and coverage by sequential seeding in complex networks"
  Abstract: Information spreading in complex networks is often modeled as diffusing information with certain probability from nodes that possess it to their neighbors that do not. Information cascades are triggered when the activation of a set of initial nodes (seeds) results in diffusion to large number of nodes. Here, several novel approaches for seed initiation that replace the commonly used activation of all seeds at once with a sequence of initiation stages are introduced. Sequential strategies at later stages avoid seeding highly ranked nodes that are already activated by diffusion active between stages. The gain arises when a saved seed is allocated to a node difficult to reach via diffusion. Sequential seeding and a single stage approach are compared using various seed ranking methods and diffusion parameters on real complex networks. The experimental results indicate that, regardless of the seed ranking method used, sequential seeding strategies deliver better coverage than single stage seeding in about 90% of cases. Longer seeding sequences tend to activate more nodes but they also extend the duration of diffusion. Various variants of sequential seeding resolve the trade-off between the coverage and speed of diffusion differently.

Connected through hyperedge HE-1 (induced by source paper "computational aspects of optimal strategic network diffusion"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "scalable methods for adaptively seeding a social network"
    - "analytical approach to parallel repetition"
    - "a data based approach to social influence maximization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "strategic attack defense in security diffusion games"):
  with members:
    - "multidefender security games"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "stochastic games for security in networks with interdependent nodes"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "identifying influential nodes based on fuzzy local dimension in complex networks"):
  with members:
    - "an evidential markov decision making model"

Connected through hyperedge HE-4 (induced by source paper "probing limits of information spread with sequential seeding"):
  with members:
    - "seeds buffering for information spreading processes"
    - "scalable methods for adaptively seeding a social network"
    - "seed selection for spread of influence in social networks temporal vs static approach"

Connected through hyperedge HE-5 (induced by source paper "influence maximization for fixed heterogeneous thresholds"):
  with members:
    - "sequential seeding in complex networks trading speed for coverage"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "a scalable heuristic for viral marketing under the tipping model"

Connected through hyperedge HE-6 (induced by source paper "seeds buffering for information spreading processes"):
  with members:
    - "identifying a set of influential spreaders in complex networks"
    - "scalable methods for adaptively seeding a social network"
    - "seed selection for spread of influence in social networks temporal vs static approach"

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)