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: "distributed active state estimation with user specified accuracy"
  Abstract: In this paper, we address the problem of controlling a network of mobile sensors so that a set of hidden states are estimated up to a user-specified accuracy. The sensors take measurements and fuse them online using an Information Consensus Filter (ICF). At the same time, the local estimates guide the sensors to their next best configuration. This leads to an LMI-constrained optimization problem that we solve by means of a new distributed random approximate projections method. The new method is robust to the state disagreement errors that exist among the robots as the ICF fuses the collected measurements. Assuming that the noise corrupting the measurements is zero-mean and Gaussian and that the robots are self localized in the environment, the integrated system converges to the next best positions from where new observations will be taken. This process is repeated with the robots taking a sequence of observations until the hidden states are estimated up to the desired user-specified accuracy. We present simulations of sparse landmark localization, where the robotic team achieves the desired estimation tolerances while exhibiting interesting emergent behavior.

Connected through hyperedge HE-1 (induced by source paper "distributed state estimation using intermittently connected robot networks"):
  with members:
    - "model based active source identification in complex environments"
    - "distributed hierarchical control for state estimation with robotic sensor networks"
    - "simultaneous intermittent communication control and path optimization in networks of mobile robots"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "decentralized estimation of laplacian eigenvalues in multi agent systems"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "model based learning of turbulent flows using mobile robots"):
  with members:
    - "model based active source identification in complex environments"
    - "distributed state estimation using intermittently connected robot networks"
    - "control of uniflagellar soft robots at low reynolds number using buckling instability"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "decentralized ergodic control distribution driven sensing and exploration for multiagent systems"):
  with members:
    - "real time area coverage and target localization using receding horizon ergodic exploration"
    - "ergodic exploration using binary sensing for nonparametric shape estimation"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "ergodic exploration of distributed information"

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)