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: "stability of influence maximization"
  Abstract: The present article serves as an erratum to our paper of the same title, which was presented and published in the KDD 2014 conference. In that article, we claimed falsely that the objective function defined in Section 1.4 is non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example to that claim. #R##N#Subsequent to becoming aware of the counter-example, we have shown that the objective function is in fact NP-hard to approximate to within a factor of $O(n^{1-\epsilon})$ for any $\epsilon > 0$. #R##N#In an attempt to fix the record, the present article combines the problem motivation, models, and experimental results sections from the original incorrect article with the new hardness result. We would like readers to only cite and use this version (which will remain an unpublished note) instead of the incorrect conference version.

Connected through hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "stability and recovery for independence systems"
    - "constrained submodular maximization beyond 1 e"
    - "deterministic algorithms for submodular maximization problems"
    - "computational feasibility of clustering under clusterability assumptions"
    - "lazier than lazy greedy"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-2 (induced by source paper "robust influence maximization for hyperparametric models"):
  with members:
    - "robust optimization for non convex objectives"
    - "model independent online learning for influence maximization"
    - "robust influence maximization"
    - "robust influence maximization"
    - "efficient learning in large scale combinatorial semi bandits"
    - "analytical approach to parallel repetition"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-3 (induced by source paper "attention models in graphs a survey"):
  with members:
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "spine structural identity preserved inductive network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "on sampling from massive graph streams"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-4 (induced by source paper "robust influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "trace complexity of network inference"
    - "analytical approach to parallel repetition"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-5 (induced by source paper "robust influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "bandits with knapsacks"
    - "maximizing social influence in nearly optimal time"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-6 (induced by source paper "robust optimization for tree structured stochastic network design"):
  with members:
    - "maximizing the spread of cascades using network design"

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)