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: "fastsir algorithm a fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible infected recovered compartment model"
  Abstract: We propose two efficient epidemic spreading algorithms (Naive SIR and FastSIR) for arbitrary network structures, based on the SIR (susceptible-infected-recovered) compartment model. The Naive SIR algorithm models full epidemic dynamics of the well-known SIR model and uses data structures efficiently to reduce running time. The FastSIR algorithm is based on the probability distribution over the number of infected nodes and uses the concept of generation time instead of explicit time in treating the spreading dynamics. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. The average case running time of both algorithms has also been derived and an experimental analysis was made on five different empirical complex networks.

Connected through hyperedge HE-1 (induced by source paper "simulating sir processes on networks using weighted shortest paths"):
  with members:
    - "measuring the potential of individual airports for pandemic spread over the world airline network"
    - "identification of patient zero in static and temporal networks robustness and limitations"
    - "locating the source of diffusion in large scale networks"
    - "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 "mapping spreading dynamics from time respecting shortest paths to bond percolation"):
  with members:
    - "locating the source of diffusion in large scale networks"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-3 (induced by source paper "statistical inference framework for source detection of contagion processes on arbitrary network structures"):
  with members:
    - "rooting out the rumor culprit from suspects"
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

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.DS(Data Structures and Algorithms)