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: "diff dac distributed actor critic for multitask deep reinforcement learning"
  Abstract: We propose a multiagent distributed actor-critic algorithm for multitask reinforcement learning (MRL), named \textit{Diff-DAC}. The agents are connected, forming a (possibly sparse) network. Each agent is assigned a task and has access to data from this local task only. During the learning process, the agents are able to communicate some parameters to their neighbors. Since the agents incorporate their neighbors' parameters into their own learning rules, the information is diffused across the network, and they can learn a common policy that generalizes well across all tasks. Diff-DAC is scalable since the computational complexity and communication overhead per agent grow with the number of neighbors, rather than with the total number of agents. Moreover, the algorithm is fully distributed in the sense that agents self-organize, with no need for coordinator node. Diff-DAC follows an actor-critic scheme where the value function and the policy are approximated with deep neural networks, being able to learn expressive policies from raw data. As a by-product of Diff-DAC's derivation from duality theory, we provide novel insights into the standard actor-critic framework, showing that it is actually an instance of the dual ascent method to approximate the solution of a linear program. Experiments illustrate the performance of the algorithm in the cart-pole, inverted pendulum, and swing-up cart-pole environments.

Connected through hyperedge HE-1 (induced by source paper "leveraging communication topologies between learning agents in deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "asynchronous methods for deep reinforcement learning"
    - "social learning strategies modify the effect of network structure on group performance"
    - "massively parallel methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "an introduction to collective intelligence"

Connected through hyperedge HE-2 (induced by source paper "communication topologies between learning agents in deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "population based training of neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "social learning strategies modify the effect of network structure on group performance"
    - "massively parallel methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "distral robust multitask reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-4 (induced by source paper "multi agent reinforcement learning via double averaging primal dual optimization"):
  with members:
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "boosting the actor with dual critic"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "stochastic variance reduction methods for policy evaluation"
    - "distral robust multitask reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation for reinforcement learning on the atari"):
  with members:
    - "transfer learning from synthetic to real images using variational autoencoders for robotic applications"
    - "progressive neural networks"
    - "mutual alignment transfer learning"
    - "adversarially regularized autoencoders"
    - "adversarial discriminative domain adaptation"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "learning to reinforcement learn"
    - "connecting generative adversarial networks and actor critic methods"

Connected through hyperedge HE-6 (induced by source paper "multi agent fully decentralized value function learning with linear convergence rates"):
  with members:
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "stochastic variance reduction methods for policy evaluation"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "next in network nonconvex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed policy evaluation under multiple behavior strategies"

Connected through hyperedge HE-7 (induced by source paper "how to organize your deep reinforcement learning agents the importance of communication topology"):
  with members:
    - "population based training of neural networks"
    - "massively parallel methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with 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)