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: "multi task deep reinforcement learning with popart"
  Abstract: The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.

Connected through hyperedge HE-1 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generating wikipedia by summarizing long sequences"
    - "differentiable plasticity training plastic neural networks with backpropagation"

Connected through hyperedge HE-2 (induced by source paper "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"):
  with members:
    - "reinforcement learning for improving agent design"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "evolving multimodal robot behavior via many stepping stones with the combinatorial multi objective evolutionary algorithm"
    - "distributed prioritized experience replay"
    - "diversity is all you need learning skills without a reward function"
    - "episodic curiosity through reachability"
    - "coevolutionary neural population models"

Connected through hyperedge HE-3 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "variance reduction for policy gradient with action dependent factorized baselines"

Connected through hyperedge HE-4 (induced by source paper "rlcache automated cache management using reinforcement learning"):
  with members:
    - "adaptive caching via deep reinforcement learning"
    - "discretizing continuous action space for on policy optimization"
    - "rlgraph modular computation graphs for deep reinforcement learning"
    - "world models"
    - "the case for automatic database administration using deep reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "model free control for distributed stream data processing using deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "simultaneously learning vision and feature based control policies for real world ball in a cup"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "maximum a posteriori policy optimisation"
    - "hindsight experience replay"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"

Connected through hyperedge HE-6 (induced by source paper "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "a generalised method for empirical game theoretic analysis"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "episodic curiosity through reachability"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "malthusian reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "ray interference a source of plateaus in deep reinforcement learning"):
  with members:
    - "the value function polytope in reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "rudder return decomposition for delayed rewards"
    - "automated curriculum learning for neural networks"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"

Connected through hyperedge HE-8 (induced by source paper "disentangled skill embeddings for reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "data efficient hierarchical reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "maximum a posteriori policy optimisation"
    - "rainbow combining improvements in deep 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)