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: "composable deep reinforcement learning for robotic manipulation"
  Abstract: Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.

Connected through hyperedge HE-1 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "centralized conflict free cooperation for connected and automated vehicles at intersections by proximal policy optimization"
    - "risk averse robust adversarial reinforcement learning"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "on simple reactive neural networks for behaviour based reinforcement learning"):
  with members:
    - "vid2param online system identification from video for robotics applications"
    - "silhonet an rgb method for 6d object pose estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "investigating human priors for playing video games"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "learning to walk via deep reinforcement learning"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-3 (induced by source paper "maximum causal entropy specification inference from demonstrations"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning task specifications from demonstrations"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "spudd stochastic planning using decision diagrams"
    - "on directed information and gambling"

Connected through hyperedge HE-4 (induced by source paper "maximum entropy regularized multi goal reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "visual reinforcement learning with imagined goals"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "energy based hindsight experience prioritization"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-5 (induced by source paper "adversarial skill networks unsupervised robot skill learning from video"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "monet unsupervised scene decomposition and representation"
    - "adaptive curriculum generation from demonstrations for sim to real visuomotor control"
    - "hierarchical reinforcement learning for concurrent discovery of compound and composable policies"
    - "unsupervised visuomotor control through distributional planning networks"
    - "learning dexterous in hand manipulation"
    - "learning actionable representations from visual observations"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"

Connected through hyperedge HE-6 (induced by source paper "plan arithmetic compositional plan vectors for multi task control"):
  with members:
    - "hierarchical visuomotor control of humanoids"
    - "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-7 (induced by source paper "dynamical distance learning for semi supervised and unsupervised skill discovery"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "self supervised learning of image embedding for continuous control"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "temporal difference models model free deep rl for model based control"
    - "visual reinforcement learning with imagined goals"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-8 (induced by source paper "active domain randomization"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "generalization and regularization in dqn"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "vadra visual adversarial domain randomization and augmentation"
    - "quantifying generalization in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "an empirical study of example forgetting during deep neural network learning"

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)