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: "hierarchical visuomotor control of humanoids"
  Abstract: We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies. The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment. For a supplementary video link, see this https URL .

Connected through hyperedge HE-1 (induced by source paper "neural embedding for physical manipulations"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "normalized diversification"
    - "interpretable latent spaces for learning from demonstration"
    - "spectral normalization for generative adversarial networks"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "neural probabilistic motor primitives for humanoid control"

Connected through hyperedge HE-2 (induced by source paper "reusable neural skill embeddings for vision guided whole body movement and object manipulation"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "robust recovery controller for a quadrupedal robot using deep reinforcement learning"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "learning agile and dynamic motor skills for legged robots"
    - "deepmind control suite"

Connected through hyperedge HE-3 (induced by source paper "plan arithmetic compositional plan vectors for multi task control"):
  with members:
    - "composable deep reinforcement learning for robotic manipulation"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "latent space policies for hierarchical reinforcement learning"
    - "task embedded control networks for few shot imitation learning"
    - "neural probabilistic motor primitives for humanoid control"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-4 (induced by source paper "sub policy adaptation for hierarchical reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "self supervised learning of image embedding for continuous control"
    - "credit assignment techniques in stochastic computation graphs"
    - "hierarchical reinforcement learning with hindsight"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning with competitive ensembles of information constrained primitives"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "neural probabilistic motor primitives for humanoid control"
    - "inferring and executing programs for visual reasoning"
    - "meta learning shared hierarchies"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-6 (induced by source paper "options as responses grounding behavioural hierarchies in multi agent rl"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "open ended learning in symmetric zero sum games"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "actor attention critic for multi agent reinforcement learning"
    - "re evaluating evaluation"

Connected through hyperedge HE-7 (induced by source paper "mcp learning composable hierarchical control with multiplicative compositional policies"):
  with members:
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "learning dexterous in hand manipulation"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "neural probabilistic motor primitives for humanoid control"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep learned path planning via randomized reward linked goals and potential space applications"):
  with members:
    - "an investigation of model free planning"
    - "learning symmetric and low energy locomotion"
    - "fast approximate clearance evaluation for kinematically constrained articulated suspension systems"
    - "sim to real learning agile locomotion for quadruped robots"
    - "addressing function approximation error in actor critic methods"
    - "learning to walk via deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emergence of locomotion behaviours in rich environments"

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)