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 "modeling theory of mind in multi agent games using adaptive feedback control"):
  Abstract: A major challenge in cognitive science and AI has been to understand how autonomous agents might acquire and predict behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the computational principles to model a Theory of Mind (ToM)? Deep learning approaches to address these questions fall short of a better understanding of the problem. In part, this is due to the black-box nature of deep networks, wherein computational mechanisms of ToM are not readily revealed. Here, we consider alternative hypotheses seeking to model how the brain might realize a ToM. In particular, we propose embodied and situated agent models based on distributed adaptive control theory to predict actions of other agents in five different game theoretic tasks (Harmony Game, Hawk-Dove, Stag-Hunt, Prisoner's Dilemma and Battle of the Exes). Our multi-layer control models implement top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We test cooperative and competitive strategies among seven different agent models (cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive and other's-model agents). We show that, compared to pure reinforcement-based strategies, probabilistic learning agents modeled on rational, predictive and other's-model phenotypes perform better in game-theoretic metrics across tasks. Our autonomous multi-agent models capture systems-level processes underlying a ToM and highlight architectural principles of ToM from a control-theoretic perspective.

Includes members:
  Member "loss aversion fosters coordination among independent reinforcement learners":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "individual specialization in multi task environments with multiagent reinforcement learners")
  Member "autonomous agents modelling other agents a comprehensive survey and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "modeling the formation of social conventions in multi agent populations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "individual specialization in multi task environments with multiagent reinforcement learners")
  Member "machine theory of mind":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "a multi agent reinforcement learning model of common pool resource appropriation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster based social reinforcement learning")
  Member "prosocial learning agents solve generalized stag hunts better than selfish ones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "other play for zero shot coordination")
  Member "multi agent reinforcement learning in sequential social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a unified game theoretic approach to multiagent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

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)