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: "boosting information spread an algorithmic approach"
  Abstract: The majority of influence maximization (IM) studies focus on targeting influential seeders to trigger substantial information spread in social networks. In this paper, we consider a new and complementary problem of how to further increase the influence spread of given seeders. Our study is motivated by the observation that direct incentives could "boost" users so that they are more likely to be influenced by friends. We study the $k$-boosting problem which aims to find $k$ users to boost so that the final "boosted" influence spread is maximized. The $k$-boosting problem is different from the IM problem because boosted users behave differently from seeders: boosted users are initially uninfluenced and we only increase their probability to be influenced. Our work also complements the IM studies because we focus on triggering larger influence spread on the basis of given seeders. Both the NP-hardness of the problem and the non-submodularity of the objective function pose challenges to the $k$-boosting problem. To tackle the problem on general graphs, we devise two efficient algorithms with the data-dependent approximation ratio. For the $k$-boosting problem on bidirected trees, we present an efficient greedy algorithm and a rounded dynamic programming that is a fully polynomial-time approximation scheme. We conduct extensive experiments using real social networks and synthetic bidirected trees. We show that boosting solutions returned by our algorithms achieves boosts of influence that are up to several times higher than those achieved by boosting solutions returned by intuitive baselines, which have no guarantee of solution quality. We also explore the "budget allocation" problem in our experiments. Compared with targeting seeders with all budget, larger influence spread is achieved when we allocation the budget to both seeders and boosted users.

Connected through hyperedge HE-1 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

Connected through hyperedge HE-2 (induced by source paper "diversifying seeds and audience in social influence maximization"):
  with members:
    - "tell me something my friends do not know diversity maximization in social networks"
    - "maximizing the diversity of exposure in a social network"
    - "balancing information exposure in social networks"
    - "node2vec scalable feature learning for networks"
    - "robust influence maximization"
    - "line large scale information network embedding"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "evolving influence maximization in evolving networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "time is what prevents everything from happening at once propagation time conscious influence maximization"
    - "outward influence and cascade size estimation in billion scale networks"
    - "online influence maximization in non stationary social networks"
    - "online influence maximization"
    - "seed selection for spread of influence in social networks temporal vs static approach"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-4 (induced by source paper "k core minimization a game theoretic approach"):
  with members:
    - "towards scalable network delay minimization"
    - "social resilience in online communities the autopsy of friendster"
    - "preventing unraveling in social networks gets harder"
    - "parameterized exact and approximation algorithms for maximum k set cover and related satisfiability problems"
    - "graph measures and network robustness"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-5 (induced by source paper "influence minimization under budget and matroid constraints extended version"):
  with members:
    - "fake news detection in social networks via crowd signals"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "maximizing the spread of cascades using network design"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-6 (induced by source paper "an efficient branch and cut algorithm for approximately submodular function maximization"):
  with members:
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-7 (induced by source paper "tracking influential nodes in time decaying dynamic interaction networks"):
  with members:
    - "real time influence maximization on dynamic social streams"
    - "tracking influential individuals in dynamic networks"
    - "submodular optimization over sliding windows"
    - "robust influence maximization"
    - "submodular maximization over sliding windows"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-8 (induced by source paper "fast maximization of non submodular monotonic functions on the integer lattice"):
  with members:
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "non monotone dr submodular function maximization"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "weakly submodular functions"
    - "lazier than lazy greedy"
    - "combinatorial auctions with decreasing marginal utilities"

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)