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: "emergent tool use from multi agent autocurricula"
  Abstract: Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can overcome obstacles using ramps. We further provide evidence that multi-agent competition may scale better with increasing environment complexity and leads to behavior that centers around far more human-relevant skills than other self-supervised reinforcement learning methods such as intrinsic motivation. Finally, we propose transfer and fine-tuning as a way to quantitatively evaluate targeted capabilities, and we compare hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests.

Connected through hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "a theoretical analysis of deep q learning"

Connected through hyperedge HE-2 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"
    - "self supervised exploration via disagreement"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"
    - "theory of curriculum learning with convex loss functions"

Connected through hyperedge HE-4 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "hierarchical reinforcement learning with advantage based auxiliary rewards"
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "skill transfer in deep reinforcement learning under morphological heterogeneity"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "hardware conditioned policies for multi robot transfer learning"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-5 (induced by source paper "multi agent reinforcement learning with emergent roles"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "influence based multi agent exploration"
    - "learning fairness in multi agent systems"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "maven multi agent variational exploration"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "neural mmo v1 3 a massively multiagent game environment for training and evaluating neural networks"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "ray a distributed framework for emerging ai applications"
    - "attention is all you need"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-7 (induced by source paper "multi agent reinforcement learning as a computational tool for language evolution research historical context and future challenges"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-8 (induced by source paper "a deep multi agent reinforcement learning approach to autonomous separation assurance"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "starcraft ii a new challenge for reinforcement learning"
    - "social attention modeling attention in human crowds"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

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.LG(Machine Learning)