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: "on the convergence of the hegselmann krause system"
  Abstract: We study convergence of the following discrete-time non-linear dynamical system: n agents are located in R^d and at every time step, each moves synchronously to the average location of all agents within a unit distance of it. This popularly studied system was introduced by Krause to model the dynamics of opinion formation and is often referred to as the Hegselmann-Krause model. We prove the first polynomial time bound for the convergence of this system in arbitrary dimensions. This improves on the bound of n^{O(n)} resulting from a more general theorem of Chazelle. Also, we show a quadratic lower bound and improve the upper bound for one-dimensional systems to O(n^3).

Connected through hyperedge HE-1 (induced by source paper "opinion dynamics on correlated subjects in social networks"):
  with members:
    - "belief dynamics in social networks a fluid based analysis"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "how agreement and disagreement evolve over random dynamic networks"

Connected through hyperedge HE-2 (induced by source paper "a simple framework for stability analysis of state dependent networks of heterogeneous agents"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "evolution of social power in social networks with dynamic topology"
    - "inertial hegselmann krause systems"
    - "convergence time of quantized metropolis consensus over time varying networks"

Connected through hyperedge HE-3 (induced by source paper "belief dynamics in social networks a fluid based analysis"):
  with members:
    - "consensus convergence with stochastic effects"
    - "bounded confidence opinion dynamics in a social network of bayesian decision makers"
    - "ergodic randomized algorithms and dynamics over networks"
    - "how agreement and disagreement evolve over random dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "a tutorial on modeling and analysis of dynamic social networks part i"):
  with members:
    - "on the analysis of the degroot friedkin model with dynamic relative interaction matrices"
    - "opinion evolution in time varying social influence networks with prejudiced agents"
    - "differential inequalities in multi agent coordination and opinion dynamics modeling"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "open multi agent systems gossiping with random arrivals and departures"
    - "active sensing of social networks"
    - "structural balance and opinion separation in trust mistrust social networks"

Connected through hyperedge HE-5 (induced by source paper "how friends and non determinism affect opinion dynamics"):
  with members:
    - "inertial hegselmann krause systems"
    - "an improved energy argument for the hegselmann krause model"
    - "a quadratic lower bound for the convergence rate in the one dimensional hegselmann krause bounded confidence dynamics"

Connected through hyperedge HE-6 (induced by source paper "an improved energy argument for the hegselmann krause model"):
  with members:
    - "game theoretic analysis of the hegselmann krause model for opinion dynamics in finite dimensions"
    - "a quadratic lower bound for the convergence rate in the one dimensional hegselmann krause bounded confidence dynamics"

Connected through hyperedge HE-7 (induced by source paper "inertial hegselmann krause systems"):
  with members:
    - "game theoretic analysis of the hegselmann krause model for opinion dynamics in finite dimensions"

Connected through hyperedge HE-8 (induced by source paper "game theoretic analysis of the hegselmann krause model for opinion dynamics in finite dimensions"):
  with members:
    - "optimal strategies in the average consensus problem"

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.DS(Data Structures and Algorithms)