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: "opinion dynamics with varying susceptibility to persuasion"
  Abstract: A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. #R##N#We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates.

Connected through hyperedge HE-1 (induced by source paper "network disruption maximizing disagreement and polarization in social networks"):
  with members:
    - "adversarial perturbations of opinion dynamics in networks"
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "minimizing polarization and disagreement in social networks"
    - "some like it hoax automated fake news detection in social networks"
    - "opinion maximization in social networks"

Connected through hyperedge HE-2 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "deepinf social influence prediction with deep learning"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-3 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks"):
  with members:
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "polarization and fake news early warning of potential misinformation targets"
    - "spread of information with confirmation bias in cyber social networks"
    - "fake news in social networks"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "network inference from consensus dynamics"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-4 (induced by source paper "shifting opinions in a social network through leader selection"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "an efficient parallel solver for sdd linear systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"

Connected through hyperedge HE-5 (induced by source paper "disagreement and polarization in two party social networks"):
  with members:
    - "shifting opinions in a social network through leader selection"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "opinion fluctuations and disagreement in social networks"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-6 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "maximizing the diversity of exposure in a social network"
    - "find your place simple distributed algorithms for community detection"
    - "opinion maximization in social networks"
    - "biased assimilation homophily and the dynamics of polarization"
    - "how bad is forming your own opinion"

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)