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: "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
  Abstract: Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing time and costs and improving treatment outcomes. Because of the large and complex solution spaces of biomechanical models, current methods are constrained to specific movements and models, requiring careful design of a controller and hindering many possible applications. We sought to discover if modern optimization methods efficiently explore these complex spaces. To do this, we posed the problem as a competition in which participants were tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible, without using any experimental data. They were provided with a human musculoskeletal model and a physics-based simulation environment. In this paper, we discuss the design of the competition, technical difficulties, results, and analysis of the top controllers. The challenge proved that deep reinforcement learning techniques, despite their high computational cost, can be successfully employed as an optimization method for synthesizing physiologically feasible motion in high-dimensional biomechanical systems.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence for prosthetics challenge solutions"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "distributed distributional deterministic policy gradients"
    - "parameter space noise for exploration"

Connected through hyperedge HE-2 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors a retrospective"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "deep reinforcement learning from policy dependent human feedback"
    - "hierarchical deep q network with forgetting from imperfect demonstrations in minecraft"
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "string diagrams for assembly planning"
    - "improving deep reinforcement learning in minecraft with action advice"
    - "keeping your distance solving sparse reward tasks using self balancing shaped rewards"

Connected through hyperedge HE-3 (induced by source paper "deep q learning with q matrix transfer learning for novel fire evacuation environment"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "unity a general platform for intelligent agents"
    - "large scale distributed neural network training through online distillation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot dual machine translation"
    - "target transfer q learning and its convergence analysis"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-4 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors"):
  with members:
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "generative design in minecraft gdmc settlement generation competition"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "rainbow combining improvements in deep reinforcement learning"
    - "one shot visual imitation learning via meta learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-5 (induced by source paper "the ai driving olympics at neurips 2018"):
  with members:
    - "addressing function approximation error in actor critic methods"
    - "active neural localization"
    - "deep reinforcement learning that matters"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "synthesis of biologically realistic human motion using joint torque actuation"):
  with members:
    - "learning symmetric and low energy locomotion"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-7 (induced by source paper "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"
    - "self normalizing neural networks"
    - "progressive neural networks"
    - "run skeleton run skeletal model in a physics based simulation"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"

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)