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: "efficient planning under uncertainty with macro actions"
  Abstract: Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions. We present a formal analysis of our approach, and examine its performance on two very large simulation experiments: scientific exploration and a target monitoring domain. We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multistep lookahead is required to achieve good performance.

Connected through hyperedge HE-1 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "energy based continuous inverse optimal control"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "belief and truth in hypothesised behaviours"
    - "watch this scalable cost function learning for path planning in urban environments"

Connected through hyperedge HE-2 (induced by source paper "a model based decision theoretic perspective on automated cyber response"):
  with members:
    - "despot online pomdp planning with regularization"
    - "online planning algorithms for pomdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers"):
  with members:
    - "bi directional value learning for risk aware planning under uncertainty"
    - "low level active visual navigation increasing robustness of vision based localization using potential fields"
    - "despot online pomdp planning with regularization"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "sampling based algorithms for optimal motion planning"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-4 (induced by source paper "adaptive skills adaptive partitions asap"):
  with members:
    - "the option critic architecture"
    - "reinforcement learning with parameterized actions"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "deep reinforcement learning in parameterized action space"
    - "hierarchical solution of markov decision processes using macro actions"
    - "compositional planning using optimal option models"
    - "learning parameterized skills"

Connected through hyperedge HE-5 (induced by source paper "motion planning for global localization in non gaussian belief spaces"):
  with members:
    - "motion planning in non gaussian belief spaces m3p the case of a kidnapped robot"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "decentralized control of partially observable markov decision processes using belief space macro actions"):
  with members:
    - "planning for decentralized control of multiple robots under uncertainty"

Connected through hyperedge HE-7 (induced by source paper "modelling observation correlations for active exploration and robust object detection"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "online planning algorithms for pomdps"

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)