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: "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
  Abstract: Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate the efficacy of our influence maximization algorithm over other heuristic algorithms.

Connected through hyperedge HE-1 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "controversy and sentiment in online news"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "the slashdot zoo mining a social network with negative edges"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-2 (induced by source paper "maximizing contrasting opinions in signed social networks"):
  with members:
    - "the wisdom of polarized crowds"
    - "minimizing polarization and disagreement in social networks"
    - "reducing controversy by connecting opposing views"
    - "a survey of signed network mining in social media"
    - "from competition to complementarity comparative influence diffusion and maximization"

Connected through hyperedge HE-3 (induced by source paper "compas community preserving sampling for streaming graphs"):
  with members:
    - "exploiting social network structure for person to person sentiment analysis"
    - "discriminative link prediction using local links node features and community structure"
    - "network sampling from static to streaming graphs"
    - "defining and evaluating network communities based on ground truth"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-4 (induced by source paper "signed network modeling based on structural balance theory"):
  with members:
    - "learning deep generative models of graphs"
    - "signed graph convolutional network"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "community structure and scale free collections of erdős renyi graphs"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "maximizing the smallest eigenvalue of a symmetric matrix a submodular optimization approach"):
  with members:
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"

Connected through hyperedge HE-6 (induced by source paper "understanding group event scheduling via the outwithfriendz mobile application"):
  with members:
    - "event organization 101 understanding latent factors of event popularity"
    - "group colocation behavior in technological social networks"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-7 (induced by source paper "using node centrality and optimal control to maximize information diffusion in social networks"):
  with members:
    - "seed selection for spread of influence in social networks temporal vs static approach"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-8 (induced by source paper "holistic influence maximization combining scalability and efficiency with opinion aware models"):
  with members:
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

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)