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 complex dexterous manipulation with deep reinforcement learning and demonstrations"
  Abstract: Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to high-dimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. Thus, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks. In this work, we show that model-free DRL with natural policy gradients can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, and enable learning within the equivalent of a few hours of robot experience. We demonstrate successful policies for multiple complex tasks: object relocation, in-hand manipulation, tool use, and door opening.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "policies modulating trajectory generators"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-4 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-5 (induced by source paper "emergent tool use from multi agent autocurricula"):
  with members:
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "structured agents for physical construction"
    - "exploration by random network distillation"

Connected through hyperedge HE-6 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-7 (induced by source paper "towards practical multi object manipulation using relational reinforcement learning"):
  with members:
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "on first order meta learning algorithms"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "modulated policy hierarchies"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "a study on overfitting in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "dynamics aware unsupervised discovery of skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "therml the thermodynamics of machine learning"
    - "model ensemble trust region policy optimization"
    - "soft actor critic algorithms and applications"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

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)