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: "cooperation and contagion in web based networked public goods experiments"
  Abstract: A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of experiments on Amazon Mechanical Turk, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial "seed" players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation did not spread beyond direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs.

Connected through hyperedge HE-1 (induced by source paper "computational human dynamics"):
  with members:
    - "joint embedding of structure and features via graph convolutional networks"
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "the temporal event graph"
    - "link transmission centrality in large scale social networks"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "graph embedding techniques applications and performance a survey"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-2 (induced by source paper "randomized experiments to detect and estimate social influence in networks"):
  with members:
    - "discussion quality diffuses in the digital public square"
    - "designing and deploying online field experiments"
    - "graph cluster randomization network exposure to multiple universes"
    - "selection effects in online sharing consequences for peer adoption"
    - "the role of social networks in information diffusion"
    - "social influence in social advertising evidence from field experiments"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "latent structure in collaboration the case of reddit r place"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "complexity and information measuring emergence self organization and homeostasis at multiple scales"

Connected through hyperedge HE-4 (induced by source paper "cooperation in the spatial prisoner s dilemma game with probabilistic abstention"):
  with members:
    - "the impact of coevolution and abstention on the emergence of cooperation"
    - "simulation of an optional strategy in the prisoner s dilemma in spatial and non spatial environments"
    - "heuristics guide the implementation of social preferences in one shot prisoner s dilemma experiments"

Connected through hyperedge HE-5 (induced by source paper "web based vr experiments powered by the crowd"):
  with members:
    - "mechanical novel crowdsourcing complex work through reflection and revision"
    - "the online laboratory conducting experiments in a real labor market"

Connected through hyperedge HE-6 (induced by source paper "network structure efficiency and performance in wikiprojects"):
  with members:
    - "social learning strategies modify the effect of network structure on group performance"
    - "laboratories of oligarchy how the iron law extends to peer production"

Connected through hyperedge HE-7 (induced by source paper "status maximization as a source of fairness in a networked dictator game"):
  with members:
    - "modelling community formation driven by the status of individual in a society"

Connected through hyperedge HE-8 (induced by source paper "collective intelligence heuristic an experimental evidence"):
  with members:
    - "the online laboratory conducting experiments in a real labor market"

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)