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: "machine theory of mind"
  Abstract: Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network -- a ToMnet -- which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents' behaviour, as well as the ability to bootstrap to richer predictions about agents' characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the "Sally-Anne" test (Wimmer & Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold false beliefs about the world. We argue that this system -- which autonomously learns how to model other agents in its world -- is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-3 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence values and alignment"):
  with members:
    - "agi safety literature review"
    - "scalable agent alignment via reward modeling a research direction"
    - "incomplete contracting and ai alignment"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"
    - "constrained policy optimization"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-5 (induced by source paper "rl agents implicitly learning human preferences"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "visualizing and understanding atari agents"
    - "graying the black box understanding dqns"

Connected through hyperedge HE-6 (induced by source paper "i feel i feel you a theory of mind experiment in games"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"

Connected through hyperedge HE-7 (induced by source paper "multi turn beam search for neural dialogue modeling"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-8 (induced by source paper "modeling conceptual understanding in image reference games"):
  with members:
    - "emergence of compositional language with deep generational transmission"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "compositional obverter communication learning from raw visual input"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "faithful multimodal explanation for visual question answering"
    - "interactive agent modeling by learning to probe"

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.AI(Artificial Intelligence)