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: "ergodic coverage in constrained environments using stochastic trajectory optimization"
  Abstract: In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.

Connected through hyperedge HE-1 (induced by source paper "active area coverage from equilibrium"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "decentralized ergodic control distribution driven sensing and exploration for multiagent systems"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-2 (induced by source paper "quantifying robotic swarm coverage"):
  with members:
    - "coverage and field estimation on bounded domains by diffusive swarms"

Connected through hyperedge HE-3 (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"
    - "real time area coverage and target localization using receding horizon ergodic exploration"
    - "ergodic exploration of distributed information"
    - "convolutional neural networks at constrained time cost"

Connected through hyperedge HE-4 (induced by source paper "on the optimality of ergodic trajectories for information gathering tasks"):
  with members:
    - "autonomous visual rendering using physical motion"
    - "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)