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: "bounded policy synthesis for pomdps with safe reachability objectives"
  Abstract: Planning robust executions under uncertainty is a fundamental challenge for building autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a standard framework for modeling uncertainty in many robot applications. A key algorithmic problem for POMDPs is policy synthesis. While this problem has traditionally been posed w.r.t. optimality objectives, many robot applications are better modeled by POMDPs where the objective is a boolean requirement. In this paper, we study the latter problem in a setting where the requirement is a safe-reachability property, which states that with a probability above a certain threshold, it is possible to eventually reach a goal state while satisfying a safety requirement. The central challenge in our problem is that it requires reasoning over a vast space of probability distributions. What's more, it has been shown that policy synthesis of POMDPs with reachability objectives is undecidable in general. To address these challenges, we introduce the notion of a goal-constrained belief space, which only contains beliefs (probability distributions over states) reachable from the initial belief under desired executions. This constrained space is generally much smaller than the original belief space. Our approach compactly represents this space over a bounded horizon using symbolic constraints, and employs an incremental Satisfiability Modulo Theories (SMT) solver to efficiently search for a valid policy over it. We evaluate our method using a case study involving a partially observable robotics domain with uncertain obstacles. Our results suggest that it is possible to synthesize policies over large belief spaces with a small number of SMT solver calls by focusing on goal-constrained belief space, and our method o ers a stronger guarantee of both safety and reachability than alternative unconstrained/constrained POMDP formulations.

Connected through hyperedge HE-1 (induced by source paper "point based methods for model checking in partially observable markov decision processes"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "a storm is coming a modern probabilistic model checker"
    - "inverse reward design"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "a theory of goal oriented mdps with dead ends"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-2 (induced by source paper "stochastic finite state control of pomdps with ltl specifications"):
  with members:
    - "counterexample guided strategy improvement for pomdps using recurrent neural networks"
    - "the partially observable games we play for cyber deception"
    - "optimal control of mdps with temporal logic constraints"
    - "qualitative analysis of partially observable markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "online replanning in belief space for partially observable task and motion problems"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "representing robot task plans as robust logical dynamical systems"
    - "sampling based methods for factored task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "an anytime algorithm for task and motion mdps"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning with probabilistic guarantees for autonomous driving"):
  with members:
    - "shielded decision making in mdps"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safe reinforcement learning via shielding"
    - "prioritized experience replay"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "cost bounded active classification using partially observable markov decision processes"):
  with members:
    - "barrier certificates for assured machine teaching"
    - "privacy verification in pomdps via barrier certificates"
    - "pomdp model learning for human robot collaboration"
    - "synthesis of surveillance strategies via belief abstraction"
    - "active classification theory and application to underwater inspection"

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)