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 when to communicate at scale in multiagent cooperative and competitive tasks"
  Abstract: Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

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"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "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"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "emergent communication with world models"):
  with members:
    - "biases for emergent communication in multi agent reinforcement learning"
    - "on the pitfalls of measuring emergent communication"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "compositional obverter communication learning from raw visual input"
    - "world models"

Connected through hyperedge HE-5 (induced by source paper "learning nearly decomposable value functions via communication minimization"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "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"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "multi agent deep reinforcement learning with adaptive policies"):
  with members:
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "flow a modular learning framework for autonomy in traffic"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-7 (induced by source paper "dealing with non stationarity in multi agent deep reinforcement learning"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "addressing function approximation error in actor critic methods"
    - "learning policy representations in multiagent systems"
    - "machine theory of mind"
    - "learning with opponent learning awareness"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-8 (induced by source paper "r maddpg for partially observable environments and limited communication"):
  with members:
    - "emergent coordination through competition"
    - "learning attentional communication for multi agent cooperation"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"

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)