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: "when is a network epidemic hard to eliminate"
  Abstract: We consider the propagation of a contagion process (epidemic) on a network and study the problem of dynamically allocating a fixed curing budget to the nodes of the graph, at each time instant. For bounded degree graphs, we provide a lower bound on the expected time to extinction under any such dynamic allocation policy, in terms of a combinatorial quantity that we call the resistance of the set of initially infected nodes, the available budget, and the number of nodes n. Specifically, we consider the case of bounded degree graphs, with the resistance growing linearly in n. We show that if the curing budget is less than a certain multiple of the resistance, then the expected time to extinction grows exponentially with n. As a corollary, if all nodes are initially infected and the CutWidth of the graph grows linearly, while the curing budget is less than a certain multiple of the CutWidth, then the expected time to extinction grows exponentially in n. The combination of the latter with our prior work establishes a fairly sharp phase transition on the expected time to extinction (sub-linear versus exponential) based on the relation between the CutWidth and the curing budget.

Connected through hyperedge HE-1 (induced by source paper "efficient containment of exact sir markovian processes on networks"):
  with members:
    - "stability of spreading processes over time varying large scale networks"
    - "epidemic processes over adaptive state dependent networks"
    - "optimal patching in clustered malware epidemics"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-2 (induced by source paper "social learning with questions"):
  with members:
    - "informational braess paradox the effect of information on traffic congestion"
    - "social learning with endogenous network formation"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "on learning with finite memory"
    - "strategic learning and the topology of social networks"

Connected through hyperedge HE-3 (induced by source paper "game theoretic vaccination against networked sis epidemics and impacts of human decision making"):
  with members:
    - "game theoretic choice of curing rates against networked sis epidemics by human decision makers"
    - "designing virus resistant high performance networks a game formation approach"
    - "interdependent security games on networks under behavioral probability weighting"
    - "effects of degree correlations in interdependent security good or bad"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "fragility of the commons under prospect theoretic risk attitudes"
    - "analysis of exact and approximated epidemic models over complex networks"
    - "networked sis epidemics with awareness"

Connected through hyperedge HE-4 (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"
    - "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 "the stochastic firefighter problem"):
  with members:
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial influence maximization"):
  with members:
    - "online influence maximization with local observations"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "learning influence functions from incomplete observations"
    - "online influence maximization"

Connected through hyperedge HE-7 (induced by source paper "protection of complex networks against sir spreading processes"):
  with members:
    - "stability of spreading processes over time varying large scale networks"
    - "epidemic processes over adaptive state dependent networks"
    - "optimal patching in clustered malware epidemics"
    - "cost optimal switching protection strategy in adaptive networks"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-8 (induced by source paper "a lower bound on the performance of dynamic curing policies for epidemics on graphs"):
  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)