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 multi agent communication under limited bandwidth restriction for internet packet routing"
  Abstract: Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to learn the communication strategy and the control policy for multiple agents. However, when implementing the communication for real-world multi-agent applications, there is a more practical limited-bandwidth restriction, which has been largely ignored by the existing DRL-based methods. Specifically, agents trained by most previous methods keep sending messages incessantly in every control cycle; due to emitting too many messages, these methods are unsuitable to be applied to the real-world systems that have a limited bandwidth to transmit the messages. To handle this problem, we propose a gating mechanism to adaptively prune unprofitable messages. Results show that the gating mechanism can prune more than 80% messages with little damage to the performance. Moreover, our method outperforms several state-of-the-art DRL-based and rule-based methods by a large margin in both the real-world packet routing tasks and four benchmark tasks.

Connected through hyperedge HE-1 (induced by source paper "learning efficient multi agent communication an information bottleneck approach"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "on the pitfalls of measuring emergent communication"
    - "learning attentional communication for multi agent cooperation"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "deep variational information bottleneck"

Connected through hyperedge HE-2 (induced by source paper "a survey of machine learning applied to computer architecture design"):
  with members:
    - "a static analysis based cross architecture performance prediction using machine learning"
    - "optimizing routerless network on chip designs an innovative learning based framework"
    - "improving branch prediction by modeling global history with convolutional neural networks"
    - "learning based application agnostic 3d noc design for heterogeneous manycore systems"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the case for learned index structures"
    - "feature selection a data perspective"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-3 (induced by source paper "hierarchical deep double q routing"):
  with members:
    - "toward packet routing with fully distributed multi agent deep reinforcement learning"
    - "reward design in cooperative multi agent reinforcement learning for packet routing"
    - "experience driven networking a deep reinforcement learning based approach"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a deep reinforcement learning approach for software defined networking routing optimization"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "reward design in cooperative multi agent reinforcement learning for packet routing"):
  with members:
    - "learning agent communication under limited bandwidth by message pruning"
    - "neighborhood cognition consistent multi agent reinforcement learning"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "analysing congestion problems in multi agent reinforcement learning"
    - "reinforcement mechanism design for e commerce"
    - "a distributional perspective on 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.MA(Multiagent Systems)