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: "an efficient curing policy for epidemics on graphs"
  Abstract: We provide a dynamic policy for the rapid containment of a contagion process modeled as an SIS epidemic on a bounded degree undirected graph with n nodes. We show that if the budget $r$ of curing resources available at each time is ${\Omega}(W)$, where $W$ is the CutWidth of the graph, and also of order ${\Omega}(\log n)$, then the expected time until the extinction of the epidemic is of order $O(n/r)$, which is within a constant factor from optimal, as well as sublinear in the number of nodes. Furthermore, if the CutWidth increases only sublinearly with n, a sublinear expected time to extinction is possible with a sublinearly increasing budget $r$.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "meta learning with relational information for short sequences"
    - "em converges for a mixture of many linear regressions"
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "correlated cascades compete or cooperate"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "can cascades be predicted"

Connected through hyperedge HE-3 (induced by source paper "disentangling mixtures of epidemics on graphs"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-4 (induced by source paper "optimal containment of epidemics in temporal and adaptive networks"):
  with members:
    - "efficient containment of exact sir markovian processes on networks"
    - "stability of spreading processes over time varying large scale networks"
    - "epidemic processes over adaptive state dependent networks"
    - "decentralized protection strategies against sis epidemics in networks"
    - "co evolutionary dynamics in social networks a case study of twitter"
    - "a convex framework for optimal investment on disease awareness in social networks"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "spectral analysis of virus spreading in random geometric networks"

Connected through hyperedge HE-5 (induced by source paper "the cost of uncertainty in curing epidemics"):
  with members:
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "when is a network epidemic hard to eliminate"
    - "spy vs spy rumor source obfuscation"
    - "distinguishing infections on different graph topologies"

Connected through hyperedge HE-6 (induced by source paper "optimal deployment of resources for maximizing impact in spreading processes"):
  with members:
    - "robust influence maximization"
    - "reconstructing parameters of spreading models from partial observations"
    - "robust influence maximization"
    - "optimizing active cyber defense"
    - "detection of cyber physical faults and intrusions from physical correlations"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-7 (induced by source paper "stability of spreading processes over time varying large scale networks"):
  with members:
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "not always sparse flooding time in partially connected mobile ad hoc networks"
    - "performance of opportunistic epidemic routing on edge markovian dynamic graphs"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-8 (induced by source paper "networked sis epidemics with awareness"):
  with members:
    - "decentralized protection strategies against sis epidemics in 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)