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: "latent space policies for hierarchical reinforcement learning"
  Abstract: We address the problem of learning hierarchical deep neural network policies for reinforcement learning. In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective. Each layer is also augmented with latent random variables, which are sampled from a prior distribution during the training of that layer. The maximum entropy objective causes these latent variables to be incorporated into the layer's policy, and the higher level layer can directly control the behavior of the lower layer through this latent space. Furthermore, by constraining the mapping from latent variables to actions to be invertible, higher layers retain full expressivity: neither the higher layers nor the lower layers are constrained in their behavior. Our experimental evaluation demonstrates that we can improve on the performance of single-layer policies on standard benchmark tasks simply by adding additional layers, and that our method can solve more complex sparse-reward tasks by learning higher-level policies on top of high-entropy skills optimized for simple low-level objectives.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for combinatorial optimization a survey"):
  with members:
    - "fast detection of maximum common subgraph via deep q learning"
    - "combinatorial optimization by graph pointer networks and hierarchical reinforcement learning"
    - "co training for policy learning"
    - "exploratory combinatorial optimization with reinforcement learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to perform local rewriting for combinatorial optimization"
    - "learning representations in model free hierarchical reinforcement learning"
    - "learning permutations with sinkhorn policy gradient"

Connected through hyperedge HE-2 (induced by source paper "learning world graphs to accelerate hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "world discovery models"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "hierarchical policy learning is sensitive to goal space design"
    - "interpretable neural predictions with differentiable binary variables"
    - "model based reinforcement learning for atari"
    - "discovering options for exploration by minimizing cover time"
    - "keyin discovering subgoal structure with keyframe based video prediction"

Connected through hyperedge HE-3 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "diversity is all you need learning skills without a reward function"
    - "kickstarting deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dynamics aware embeddings"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "learning action representations for reinforcement learning"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "the natural language of actions"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

Connected through hyperedge HE-5 (induced by source paper "regularized hierarchical policies for compositional transfer in robotics"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "multitask soft option learning"
    - "dac the double actor critic architecture for learning options"
    - "information asymmetry in kl regularized rl"
    - "the termination critic"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-6 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hierarchical imitation and reinforcement learning"
    - "neural modular control for embodied question answering"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-7 (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"
    - "hierarchical visuomotor control of humanoids"
    - "spectral normalization for generative adversarial networks"
    - "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-8 (induced by source paper "multitask soft option learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "data efficient hierarchical reinforcement learning"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "diversity is all you need learning skills without a reward function"
    - "learning to explore with meta policy gradient"
    - "evolved policy gradients"
    - "variational option discovery 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.LG(Machine Learning)