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 "a meta analysis of tournaments and an evaluation of performance in the iterated prisoner s dilemma"):
  Abstract: The Iterated Prisoner's Dilemma has been used for decades as a model of behavioural interactions. From the celebrated performance of Tit for Tat, to the introduction of the zero-determinant strategies, to the use of sophisticated structures such as neural networks, the literature has been exploring the performance of strategies in the game for years. The results of the literature, however, have been relying on the performance of specific strategies in a finite number of tournaments. This manuscript evaluates 195 strategies' effectiveness in more than 40000 tournaments. The top ranked strategies are presented, and moreover, the impact of features on their success are analysed using machine learning techniques. The analysis determines that the cooperation ratio of a strategy in a given tournament compared to the mean and median cooperator is the most important feature. The conclusions are distinct for different types of tournaments. For instance a strategy with a theory of mind would aim to be the mean/median cooperator in standard tournaments, whereas in tournaments with probabilistic ending it would aim to cooperate 10% of the times the median cooperator did.

Includes members:
  Member "reinforcement learning produces dominant strategies for the iterated prisoner s dilemma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stability of defection optimisation of strategies and the limits of memory in the prisoner s dilemma")
  Member "re run repeat reproduce reuse replicate transforming code into scientific contributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perils of zero interaction security in the internet of things")
  Member "the numpy array a structure for efficient numerical computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")

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.GT(Computer Science and Game Theory)