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: "decentralized control of partially observable markov decision processes using belief space macro actions"
  Abstract: The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "the limits and potentials of deep learning for robotics"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-2 (induced by source paper "the shapeshifter a morphing multi agent multi modal robotic platform for the exploration of titan preprint version"):
  with members:
    - "bi directional value learning for risk aware planning under uncertainty"
    - "robust collaborative object transportation using multiple mavs"
    - "real time stochastic kinodynamic motion planning via multiobjective search on gpus"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-3 (induced by source paper "learning scalable and transferable multi robot machine sequential assignment planning via graph embedding"):
  with members:
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "review of multi agent algorithms for collective behavior a structural taxonomy"
    - "parameter space noise for exploration"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "option critic in cooperative multi agent systems"):
  with members:
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "the option critic architecture"
    - "adam a method for stochastic optimization"
    - "learning to cooperate via policy search"
    - "generating sequences with recurrent neural networks"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-5 (induced by source paper "risk aware reasoning for autonomous vehicles"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "predicting driver attention in critical situations"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "training a network to attend like human drivers saves it from common but misleading loss functions"
    - "the social dilemma of autonomous vehicles"
    - "shallow and deep convolutional networks for saliency prediction"
    - "instance level segmentation for autonomous driving with deep densely connected mrfs"

Connected through hyperedge HE-6 (induced by source paper "intelligent knowledge distribution constrained action pomdps for resource aware multi agent communication"):
  with members:
    - "solving pomdps by searching in policy space"
    - "improved memory bounded dynamic programming for decentralized pomdps"

Connected through hyperedge HE-7 (induced by source paper "decentralized cooperative planning for automated vehicles with hierarchical monte carlo tree search"):
  with members:
    - "learning for multi robot cooperation in partially observable stochastic environments with macro actions"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-8 (induced by source paper "review of multi agent algorithms for collective behavior a structural taxonomy"):
  with members:
    - "on distributed cooperative decision making in multiarmed bandits"
    - "on the fundamental limitations of performance for distributed decision making in robotic networks"

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)