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: "learning attentional communication for multi agent cooperation"
  Abstract: Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely helps, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.

Connected through hyperedge HE-1 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "learning nearly decomposable value functions via communication minimization"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "learning transferable cooperative behavior in multi agent teams"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (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"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"

Connected through hyperedge HE-5 (induced by source paper "badger learning to learn learning algorithms through multi agent communication"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning of sequential strategies"
    - "modularity as a means for complexity management in neural networks learning"
    - "a mathematical theory of cooperative communication"
    - "learning to communicate in multi agent reinforcement learning a review"
    - "mastering atari go chess and shogi by planning with a learned model"

Connected through hyperedge HE-6 (induced by source paper "xoc explainable observer classifier for explainable binary decisions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "neural network attributions a causal perspective"
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tarmac targeted multi agent communication"
    - "interpreting cnns via decision trees"

Connected through hyperedge HE-7 (induced by source paper "pic permutation invariant critic for multi agent deep reinforcement learning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "factor graph attention"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "link prediction based on graph neural networks"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "multi agent common knowledge 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.AI(Artificial Intelligence)