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: "identification of patient zero in static and temporal networks robustness and limitations"
  Abstract: Detection of patient zero can give new insights to epidemiologists about the nature of first transmissions into a population. In this Letter, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimators, we demonstrate the detectability limits for the susceptible infected-recovered model, which primarily depend on the spreading process characteristics. Finally, we demonstrate the applicability of the approach in a case of a simulated sexually transmitted infection spreading over an empirical temporal network of sexual interactions.

Connected through hyperedge HE-1 (induced by source paper "approximate identification of the optimal epidemic source in complex networks"):
  with members:
    - "the spread of low credibility content by social bots"
    - "dandelion redesigning the bitcoin network for anonymity"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "the spread of fake news by social bots"
    - "multiple infection sources identification with provable guarantees"
    - "how to identify an infection source with limited observations"
    - "information source detection in the sir model a sample path based approach"
    - "social structure of facebook networks"

Connected through hyperedge HE-2 (induced by source paper "ensemble approach for generalized network dismantling"):
  with members:
    - "generalized network dismantling"
    - "underestimated cost of targeted attacks on complex networks"
    - "recent advances in graph partitioning"
    - "spin glass approach to the feedback vertex set problem"

Connected through hyperedge HE-3 (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"
    - "fastsir algorithm a fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible infected recovered compartment model"
    - "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"

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)