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: "planning for decentralized control of multiple robots under uncertainty"
  Abstract: We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function. Decentralized, partially observable Markov decision processes (Dec-POMDPs) are a general model of decision processes where a team of agents must cooperate to optimize some objective (specified by a shared reward or cost function) in the presence of uncertainty, but where communication limitations mean that the agents cannot share their state, so execution must proceed in a decentralized fashion. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved as a Dec-POMDP. We describe this general model, and show how, in contrast to most existing methods that are specialized to a particular problem class, it can synthesize control policies that use whatever opportunities for coordination are present in the problem, while balancing off uncertainty in outcomes, sensor information, and information about other agents. We use three variations on a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate.

Connected through hyperedge HE-1 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "distributed prioritized experience replay"
    - "evaluating and modelling hanabi playing agents"

Connected through hyperedge HE-2 (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"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning from my partner s actions roles in decentralized robot teams"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "fully decentralized policies for multi agent systems an information theoretic approach"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "cooperative inverse reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "optimal control of two player systems with output feedback"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-4 (induced by source paper "policy gradient with value function approximation for collective multiagent planning"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "learning functions across many orders of magnitudes"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "individual planning in agent populations exploiting anonymity and frame action hypergraphs"
    - "learning to cooperate via policy search"

Connected through hyperedge HE-5 (induced by source paper "embodied evolution in collective robotics a review"):
  with members:
    - "robots that can adapt like animals"
    - "comparison of selection methods in on line distributed evolutionary robotics"
    - "an introduction to collective intelligence"

Connected through hyperedge HE-6 (induced by source paper "learning for multi robot cooperation in partially observable stochastic environments with macro actions"):
  with members:
    - "stick breaking policy learning in dec pomdps"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "anytime planning for decentralized pomdps using expectation maximization"

Connected through hyperedge HE-7 (induced by source paper "stick breaking policy learning in dec pomdps"):
  with members:
    - "anytime planning for decentralized pomdps using expectation maximization"
    - "policy iteration for decentralized control of markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "decentralized control of partially observable markov decision processes using belief space macro actions"):
  with members:
    - "efficient planning under uncertainty with macro actions"

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.RO(Robotics)