human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "can other people make you less creative"):
  Abstract: Abstract This paper explains in layperson’s terms how an agent-based model was used to investigate the hypothesis that culture evolves more effectively when novelty-generating creative processes are tempered by imitation processes that preserve proven successful ideas. Using EVOC, an agent-based model of cultural evolution we found that (1) the optimal ratio of inventing to imitating ranged from 1:1 to 2:1 depending on the fitness function, (2) there was a trade-off between the proportion of creators to conformers and how creative the creators were, and (3) when agents in increased or decreased their creativity depending on the success of their latest creative efforts, they segregated into creators and conformers, and the mean fitness of ideas across the society was higher. It is tentatively suggested that through the unconscious use of social cues, members of a society self-organizes to achieve a balanced mix of creators and conformers. Do you feel more creative in some social environments than others, or even when you are completely alone? Although it is widely believed that stimulating environments enhance creativity there is evidence that this is not always the case; indeed it turns out that creativity can be enhanced by spending time in an isolation tank blocked off from sensory stimulation entirely (Forgays & Forgays, 1992; Norlander, Bergman, & Archer, 1998; Vartanian, & Suedfeld, 2011). This suggests that social stimulation may not always enhance creativity, and may even decrease it. The idea that social environments could interfere with creativity came from thinking about creativity in the context of culture as an evolutionary process. Evolutionary processes require a balance between exploratory processes that generate new variants and conservative processes that perpetuate ‘tried and true’ variants. This is the case in biological evolution, and it seemed reasonable that it would also be the case for cultural evolution. In other words: people may send out social signals to each other that ensure that creativity---the process that fuels cultural novelty---is properly balanced by conformity,i.e., imitation of ideas that have already proven to be successful. The notion that society might aim to temper novelty-generating creativity with novelty-perpetuating imitation flies in the face of the widespread assumption that more creativity is necessarily better. It goes without saying that creativity is a good thing, and that everyone should be creative. Or does it? Sure, our capacity for self-expression, for

Includes members:
  Member "computational evidence that self regulation of creativity is good for society":
    also connected through other hyperedges:
      (none)
  Member "society functions best with an intermediate level of creativity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computational evidence that self regulation of creativity is good for society")
  Member "meme and variations a computer model of cultural evolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling the socialization of creative agents in a master apprentice setting the case of movie title puns")
  Member "how creative should creators be to optimize the evolution of ideas a computational model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cultural evolution as distributed computation")

Question: Please tell me which class the source paper 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.MA(Multiagent Systems)