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: "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
  Abstract: We consider an SIS-type epidemic process that evolves on a known graph. We assume that a fixed curing budget can be allocated at each instant to the nodes of the graph, towards the objective of minimizing the expected extinction time of the epidemic. We provide a lower bound on the optimal expected extinction time as a function of the available budget, the epidemic parameters, the maximum degree, and the CutWidth of the graph. For graphs with large CutWidth (close to the largest possible), and under a budget which is sublinear in the number of nodes, our lower bound scales exponentially with the size of the graph.

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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"
    - "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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "an efficient curing policy for epidemics on graphs"

Connected through hyperedge HE-4 (induced by source paper "the cost of uncertainty in curing epidemics"):
  with members:
    - "when is a network epidemic hard to eliminate"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"
    - "distinguishing infections on different graph topologies"

Connected through hyperedge HE-5 (induced by source paper "error correction coding meets cyber physical systems message passing analysis of self healing interdependent networks"):
  with members:
    - "message passing for analysis and resilient design of self healing interdependent cyber physical networks"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "xheal localized self healing using expanders"

Connected through hyperedge HE-6 (induced by source paper "when is a network epidemic hard to eliminate"):
  with members:
    - "an efficient curing policy for epidemics on graphs"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary 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)