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: "real time area coverage and target localization using receding horizon ergodic exploration"
  Abstract: Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.

Connected through hyperedge HE-1 (induced by source paper "using neural networks to generate information maps for mobile sensors"):
  with members:
    - "efficient and low cost localization of radio signals with a multirotor uav"
    - "ergodic coverage in constrained environments using stochastic trajectory optimization"
    - "ergodic exploration of distributed information"
    - "convolutional neural networks at constrained time cost"

Connected through hyperedge HE-2 (induced by source paper "decentralized ergodic control distribution driven sensing and exploration for multiagent systems"):
  with members:
    - "distributed active state estimation with user specified accuracy"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "data driven measurement models for active localization in sparse environments"):
  with members:
    - "ergodic exploration using binary sensing for nonparametric shape estimation"
    - "synthesizing training data for object detection in indoor scenes"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-4 (induced by source paper "distributed area coverage control with imprecise robot localization simulation and experimental studies"):
  with members:
    - "voronoi based coverage control of heterogeneous disk shaped robots"

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)