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: "decision theory with resource bounded agents"
  Abstract: There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein, charges an agent for doing costly computation; the second, initiated by Neyman, does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach, we take the objects of choice in a decision problem to be Turing machines, and charge players for the ``complexity'' of the Turing machine chosen (e.g., its running time). This approach can be used to explain well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on) and belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions) as the outcomes of quite rational decisions. For the second approach, we model people as finite automata, and provide a simple algorithm that, on a problem that captures a number of settings of interest, provably performs optimally as the number of states in the automaton increases.

Connected through hyperedge HE-1 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule"):
  with members:
    - "investigating the characteristics of one sided matching mechanisms under various preferences and risk attitudes"
    - "manipulating the probabilistic serial rule"
    - "social welfare in one sided matching mechanisms"
    - "an axiomatic approach to characterizing and relaxing strategyproofness of one sided matching mechanisms"
    - "the computational complexity of random serial dictatorship"
    - "eliminating the weakest link making manipulation intractable"
    - "social welfare in one sided matching markets without money"
    - "truthful assignment without money"

Connected through hyperedge HE-2 (induced by source paper "when humans aren t optimal robots that collaborate with risk aware humans"):
  with members:
    - "asking easy questions a user friendly approach to active reward learning"
    - "better than demonstrator imitation learning via automatically ranked demonstrations"
    - "enabling robots to infer how end users teach and learn through human robot interaction"
    - "ranking based reward extrapolation without rankings"
    - "controlling assistive robots with learned latent actions"
    - "learning reward functions by integrating human demonstrations and preferences"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "capir collaborative action planning with intention recognition"

Connected through hyperedge HE-3 (induced by source paper "beyond local nash equilibria for adversarial networks"):
  with members:
    - "evaluating gans via duality"
    - "generative adversarial networks an overview"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "fictitious gan training gans with historical models"
    - "multi agent diverse generative adversarial networks"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "research priorities for robust and beneficial artificial intelligence"):
  with members:
    - "toward idealized decision theory"
    - "friendly artificial intelligence the physics challenge"
    - "practical verification of decision making in agent based autonomous systems"
    - "game theory with translucent players"
    - "reasoning about beliefs and actions under computational resource constraints"
    - "quasi newton methods a new direction"
    - "i don t want to think about it now decision theory with costly computation"
    - "provably bounded optimal agents"

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)