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: "evolutionary dynamics of information diffusion over social networks"
  Abstract: Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users' decisions, actions, and socio-economic interactions are generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree, and nonuniform degree networks, with the highlight of two special networks, the Erdos-Renyi random network and the Barabasi-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and all the three dynamics are same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.

Connected through hyperedge HE-1 (induced by source paper "modeling of spatio temporal hawkes processes with randomized kernels"):
  with members:
    - "reinforcement learning of spatio temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "multivariate spatiotemporal hawkes processes and network reconstruction"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "modeling heterogeneous network interference using poisson point processes"

Connected through hyperedge HE-2 (induced by source paper "a graphical evolutionary game approach to social learning"):
  with members:
    - "bayesian quadratic network game filters"
    - "graphical evolutionary game for information diffusion over social networks"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "on learning with finite memory"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-3 (induced by source paper "complex network theoretical analysis on information dissemination over vehicular networks"):
  with members:
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "graphical evolutionary game for information diffusion over social networks"

Connected through hyperedge HE-4 (induced by source paper "access strategy in super wifi network powered by solar energy harvesting a pomdp method"):
  with members:
    - "data driven stochastic models and policies for energy harvesting sensor communications"
    - "graphical evolutionary game for information diffusion over social networks"

Connected through hyperedge HE-5 (induced by source paper "aggressive congestion control mechanism for space systems"):
  with members:
    - "graphical evolutionary game for information diffusion over social networks"

Connected through hyperedge HE-6 (induced by source paper "infection spreading and source identification a hide and seek game"):
  with members:
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "how to identify an infection source with limited observations"
    - "spy vs spy rumor source obfuscation"
    - "graphical evolutionary game for information diffusion over social networks"
    - "identifying infection sources and regions in large networks"
    - "rooting out the rumor culprit from suspects"

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.GT(Computer Science and Game Theory)