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: "stabilising experience replay for deep multi agent reinforcement learning"
  Abstract: Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods typically scale poorly in the problem size. Therefore, a key challenge is to translate the success of deep learning on single-agent RL to the multi-agent setting. A major stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep Q-learning relies. This paper proposes two methods that address this problem: 1) using a multi-agent variant of importance sampling to naturally decay obsolete data and 2) conditioning each agent's value function on a fingerprint that disambiguates the age of the data sampled from the replay memory. Results on a challenging decentralised variant of StarCraft unit micromanagement confirm that these methods enable the successful combination of experience replay with multi-agent RL.

Connected through hyperedge HE-1 (induced by source paper "multi agent reinforcement learning with emergent roles"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "emergent tool use from multi agent autocurricula"
    - "influence based multi agent exploration"
    - "learning fairness in multi agent systems"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "maven multi agent variational exploration"

Connected through hyperedge HE-2 (induced by source paper "other play for zero shot coordination"):
  with members:
    - "adversarially guided self play for adopting social conventions"
    - "plannable approximations to mdp homomorphisms equivariance under actions"
    - "the hanabi challenge a new frontier for ai research"
    - "improving policies via search in cooperative partially observable games"
    - "on the utility of learning about humans for human ai coordination"
    - "to populate is to regulate"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "learning agile and dynamic motor skills for legged robots"
    - "enabling intuitive human robot teaming using augmented reality and gesture control"
    - "cleaning tasks knowledge transfer between heterogeneous robots a deep learning approach"
    - "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"
    - "a comprehensive review of shepherding as a bio inspired swarm robotics guidance approach"
    - "learning attentional communication for multi agent cooperation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-4 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-5 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "learning with opponent learning awareness"
    - "a generic approach for escaping saddle points"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "extended markov games to learn multiple tasks in multi agent reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning for ltlf ldlf goals"
    - "hierarchical deep multiagent reinforcement learning"
    - "guided deep reinforcement learning for swarm systems"
    - "environment independent task specifications via gltl"
    - "safety aware apprenticeship learning"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "entropy based independent learning in anonymous multi agent settings"):
  with members:
    - "policy gradient with value function approximation for collective multiagent planning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

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.AI(Artificial Intelligence)