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: "dac the double actor critic architecture for learning options"
  Abstract: We reformulate the option framework as two parallel augmented MDPs. Under this novel formulation, all policy optimization algorithms can be used off the shelf to learn intra-option policies, option termination conditions, and a master policy over options. We apply an actor-critic algorithm on each augmented MDP, yielding the Double Actor-Critic (DAC) architecture. Furthermore, we show that, when state-value functions are used as critics, one critic can be expressed in terms of the other, and hence only one critic is necessary. We conduct an empirical study on challenging robot simulation tasks. In a transfer learning setting, DAC outperforms both its hierarchy-free counterpart and previous gradient-based option learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "continuous discrete reinforcement learning for hybrid control in robotics"):
  with members:
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "hybrid actor critic reinforcement learning in parameterized action space"
    - "multi pass q networks for deep reinforcement learning with parameterised action spaces"
    - "the termination critic"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "parametrized deep q networks learning reinforcement learning with discrete continuous hybrid action space"

Connected through hyperedge HE-2 (induced by source paper "off policy maximum entropy reinforcement learning soft actor critic with advantage weighted mixture policy sac awmp"):
  with members:
    - "planning with expectation models"
    - "teach biped robots to walk via gait principles and reinforcement learning with adversarial critics"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "model primitive hierarchical lifelong reinforcement learning"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "continual lifelong learning with neural networks a review"
    - "norml no reward meta learning"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "deepmind control suite"
    - "count based exploration with the successor representation"
    - "safe option critic learning safety in the option critic architecture"
    - "neural processes"

Connected through hyperedge HE-4 (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"
    - "information asymmetry in kl regularized rl"
    - "the termination critic"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement 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)