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: "maximizing the spread of cascades using network design"
  Abstract: We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dynamics by adding nodes or edges to the network. Our motivating application is one in spatial conservation planning, where a cascade models the dispersal of wild animals through a fragmented landscape. We propose a mixed integer programming (MIP) formulation that combines elements from network design and stochastic optimization. Our approach results in solutions with stochastic optimality guarantees and points to conservation strategies that are fundamentally different from naive approaches.

Connected through hyperedge HE-1 (induced by source paper "election manipulation on social networks seeding edge removal edge addition"):
  with members:
    - "election control in social networks via edge addition or removal"
    - "controlling elections through social influence"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "minority becomes majority in social networks"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-2 (induced by source paper "inducing equilibria in networked public goods games through network structure modification"):
  with members:
    - "election control in social networks via edge addition or removal"
    - "computing equilibria in binary networked public goods games"
    - "reducing controversy by connecting opposing views"
    - "pricing public goods for private sale"

Connected through hyperedge HE-3 (induced by source paper "influence minimization under budget and matroid constraints extended version"):
  with members:
    - "fake news detection in social networks via crowd signals"
    - "boosting information spread an algorithmic approach"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence for social good"):
  with members:
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "causal transportability of experiments on controllable subsets of variables z transportability"
    - "a sound and complete algorithm for learning causal models from relational data"

Connected through hyperedge HE-5 (induced by source paper "xor sampling for network design with correlated stochastic events"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "improving the accuracy and efficiency of map inference for markov logic"

Connected through hyperedge HE-6 (induced by source paper "recommending links through influence maximization"):
  with members:
    - "selecting nodes and buying links to maximize the information diffusion in a network"
    - "a survey of link recommendation for social networks methods theoretical foundations and future research directions"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-7 (induced by source paper "optimal resource allocation over time and degree classes for maximizing information dissemination in social networks"):
  with members:
    - "epidemic spreading with external agents"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "epidemic thresholds with external agents"

Connected through hyperedge HE-8 (induced by source paper "solving marginal map problems with np oracles and parity constraints"):
  with members:
    - "decomposition bounds for marginal map"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"

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)