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: "information acquisition with sensing robots algorithms and error bounds"
  Abstract: Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.

Connected through hyperedge HE-1 (induced by source paper "decentralized multi target tracking in urban environments overview and challenges"):
  with members:
    - "towards search based motion planning for micro aerial vehicles"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "sensor selection for target tracking in wireless sensor networks with uncertainty"
    - "the cauchy schwarz divergence for poisson point processes"
    - "cooperative multi target localization with noisy sensors"
    - "point based pomdp algorithms improved analysis and implementation"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-2 (induced by source paper "distributed state estimation using intermittently connected robot networks"):
  with members:
    - "model based active source identification in complex environments"
    - "distributed active state estimation with user specified accuracy"
    - "distributed hierarchical control for state estimation with robotic sensor networks"
    - "simultaneous intermittent communication control and path optimization in networks of mobile robots"
    - "decentralized estimation of laplacian eigenvalues in multi agent systems"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "a successive elimination approach to adaptive robotic source seeking"):
  with members:
    - "model based active source identification in complex environments"
    - "multi armed bandits with metric movement costs"
    - "ergodic exploration of distributed information"
    - "best of k bandits"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-4 (induced by source paper "resilient active information gathering with mobile robots"):
  with members:
    - "robust monotone submodular function maximization"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-5 (induced by source paper "distributed hierarchical control for state estimation with robotic sensor networks"):
  with members:
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-6 (induced by source paper "planning for robotic exploration based on forward simulation"):
  with members:
    - "optimal sensing via multi armed bandit relaxations in mixed observability domains"
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "myopic policy bounds for information acquisition pomdps"):
  with members:
    - "optimal sensing via multi armed bandit relaxations in mixed observability domains"
    - "active sequential hypothesis testing"
    - "online planning algorithms for pomdps"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "optimal sensing via multi armed bandit relaxations in mixed observability domains"):
  with members:
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "value function approximations for partially observable markov decision processes"

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.SY(Systems and Control)