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: "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
  Abstract: Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This paper addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multi-agent deep RL (MADRL) is presented, including non-stationarity, partial observability, continuous state and action spaces, multi-agent training schemes, multi-agent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed, with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to future development of more robust and highly useful multi-agent learning methods for solving real-world problems.

Connected through hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"
    - "a multi objective deep reinforcement learning framework"

Connected through hyperedge HE-2 (induced by source paper "a visual communication map for multi agent deep reinforcement learning"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "provable self play algorithms for competitive reinforcement learning"):
  with members:
    - "reward free exploration for reinforcement learning"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "corruption robust exploration in episodic reinforcement learning"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "feature based q learning for two player stochastic games"

Connected through hyperedge HE-4 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "deep learning on mobile devices a review"
    - "layered sgd a decentralized and synchronous sgd algorithm for scalable deep neural network training"
    - "towards federated learning at scale system design"
    - "multi modality sensor data classification with selective attention"
    - "analyzing federated learning through an adversarial lens"
    - "deep learning towards mobile applications"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-5 (induced by source paper "a deep multi agent reinforcement learning approach to autonomous separation assurance"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "starcraft ii a new challenge for reinforcement learning"
    - "social attention modeling attention in human crowds"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-6 (induced by source paper "evolution of a complex predator prey ecosystem on large scale multi agent deep reinforcement learning"):
  with members:
    - "an empirical study of ai population dynamics with million agent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "reward design for driver repositioning using multi agent reinforcement learning"):
  with members:
    - "multi agent reinforcement learning for order dispatching via order vehicle distribution matching"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "optimal passenger seeking policies on e hailing platforms using markov decision process and imitation learning"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "multi type mean field reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "decentralised learning in systems with many many strategic agents"
    - "deep reinforcement learning a brief survey"

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)