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: "mcp learning composable hierarchical control with multiplicative compositional policies"
  Abstract: Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be able to extract reusable skills from past experience that can be recombined in new ways for subsequent tasks. Furthermore, when controlling complex high-dimensional morphologies, such as humanoid bodies, tasks often require coordination of multiple skills simultaneously. Learning discrete primitives for every combination of skills quickly becomes prohibitive. Composable primitives that can be recombined to create a large variety of behaviors can be more suitable for modeling this combinatorial explosion. In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors. Our method factorizes an agent's skills into a collection of primitives, where multiple primitives can be activated simultaneously via multiplicative composition. This flexibility allows the primitives to be transferred and recombined to elicit new behaviors as necessary for novel tasks. We demonstrate that MCP is able to extract composable skills for highly complex simulated characters from pre-training tasks, such as motion imitation, and then reuse these skills to solve challenging continuous control tasks, such as dribbling a soccer ball to a goal, and picking up an object and transporting it to a target location.

Connected through hyperedge HE-1 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "dynamics aware unsupervised discovery of skills"
    - "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-2 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"
    - "optimizing agent behavior over long time scales by transporting value"

Connected through hyperedge HE-3 (induced by source paper "neural embedding for physical manipulations"):
  with members:
    - "normalized diversification"
    - "interpretable latent spaces for learning from demonstration"
    - "hierarchical visuomotor control of humanoids"
    - "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-4 (induced by source paper "reusable neural skill embeddings for vision guided whole body movement and object manipulation"):
  with members:
    - "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"
    - "hierarchical visuomotor control of humanoids"
    - "deepmind control suite"

Connected through hyperedge HE-5 (induced by source paper "sub policy adaptation for hierarchical reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "credit assignment techniques in stochastic computation graphs"
    - "hierarchical reinforcement learning with hindsight"
    - "hierarchical visuomotor control of humanoids"
    - "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-6 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "curious ilqr resolving uncertainty in model based rl"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "learning agile and dynamic motor skills for legged robots"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "sim to real learning agile locomotion for quadruped robots"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-7 (induced by source paper "composing task agnostic policies with deep reinforcement learning"):
  with members:
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "learning to compose skills"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "stochastic neural networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "a boolean task algebra for reinforcement learning"):
  with members:
    - "composing entropic policies using divergence correction"
    - "composable deep reinforcement learning for robotic manipulation"
    - "hindsight experience replay"
    - "the option critic architecture"
    - "successor features for transfer in reinforcement learning"
    - "learning to navigate in complex environments"
    - "taming the noise in reinforcement learning via soft updates"
    - "end to end training of deep visuomotor policies"

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)