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: "from competition to complementarity comparative influence diffusion and maximization"
  Abstract: Influence maximization is a well-studied problem that asks for a small set of influential users from a social network, such that by targeting them as early adopters, the expected total adoption through influence cascades over the network is maximized. However, almost all prior work focuses on cascades of a single propagating entity or purely-competitive entities. In this work, we propose the Comparative Independent Cascade (Com-IC) model that covers the full spectrum of entity interactions from competition to complementarity. In Com-IC, users' adoption decisions depend not only on edge-level information propagation, but also on a node-level automaton whose behavior is governed by a set of model parameters, enabling our model to capture not only competition, but also complementarity, to any possible degree. We study two natural optimization problems, Self Influence Maximization and Complementary Influence Maximization, in a novel setting with complementary entities. Both problems are NP-hard, and we devise efficient and effective approximation algorithms via non-trivial techniques based on reverse-reachable sets and a novel "sandwich approximation". The applicability of both techniques extends beyond our model and problems. Our experiments show that the proposed algorithms consistently outperform intuitive baselines in four real-world social networks, often by a significant margin. In addition, we learn model parameters from real user action logs.

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"
    - "boosting information spread an algorithmic approach"

Connected through hyperedge HE-2 (induced by source paper "maximizing welfare in social networks under a utility driven influence diffusion model"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "inferring networks of substitutable and complementary products"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-3 (induced by source paper "on multi cascade influence maximization model hardness and algorithmic framework"):
  with members:
    - "on misinformation containment in online social networks"
    - "balancing information exposure in social networks"
    - "distributed rumor blocking with multiple positive cascades"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "correlated cascades compete or cooperate"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-4 (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"
    - "boosting information spread an algorithmic approach"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "balancing spreads of influence in a social network"):
  with members:
    - "coverage centrality maximization in undirected networks"
    - "tell me something my friends do not know diversity maximization in social networks"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "maximizing the diversity of exposure in a social network"
    - "balancing information exposure in social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "the densest k subhypergraph problem"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-6 (induced by source paper "influence maximization with spontaneous user adoption"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "multi round influence maximization"
    - "robust influence maximization"
    - "robust influence maximization"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-7 (induced by source paper "scalable lattice influence maximization"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "gradient methods for submodular maximization"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "how to influence people with partial incentives"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-8 (induced by source paper "approximation algorithms for coordinating ad campaigns on social networks"):
  with members:
    - "optimizing ad allocation in social advertising"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "maximizing social influence in nearly optimal time"
    - "symmetry and approximability of submodular maximization problems"
    - "competitive contagion in networks"
    - "submodular maximization by simulated annealing"
    - "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)