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: "controlling rigid formations of mobile agents under inconsistent measurements"
  Abstract: Despite the great success of using gradient-based controllers to stabilize rigid formations of autonomous agents in the past years, surprising yet intriguing undesirable collective motions have been reported recently, when inconsistent measurements are used in the agents’ local controllers. To make the existing gradient control robust against such measurement inconsistency, we exploit local estimators following the well-known internal model principle for robust output regulation control. The new estimator-based gradient control is still distributed in nature, and can be constructed systematically even when the number of agents in a rigid formation grows. We prove rigorously that the proposed control is able to guarantee exponential convergence, and then demonstrate through robotic experiments and computer simulations that the reported inconsistency-induced orbits of collective movements are effectively eliminated.

Connected through hyperedge HE-1 (induced by source paper "bias estimation in sensor networks"):
  with members:
    - "distributed observers for lti systems"
    - "network flows that solve linear equations"
    - "robust decentralized state estimation and tracking for power systems via network gossiping"

Connected through hyperedge HE-2 (induced by source paper "flexible collaborative transportation by a team of rotorcraft"):
  with members:
    - "on board communication based relative localization for collision avoidance in micro air vehicle teams"
    - "distributed rotational and translational maneuvering of rigid formations and their applications"

Connected through hyperedge HE-3 (induced by source paper "multi robot motion formation distributed control with sensor self calibration experimental validation"):
  with members:
    - "robust collaborative object transportation using multiple mavs"
    - "safe certificate based maneuvers for teams of quadrotors using differential flatness"
    - "distributed rotational and translational maneuvering of rigid formations and their applications"
    - "distributed scaling control of rigid formations"
    - "the robotarium a remotely accessible swarm robotics research testbed"

Connected through hyperedge HE-4 (induced by source paper "on the stability and applications of distance based flexible formations"):
  with members:
    - "distributed algorithm for controlling scale free polygonal formations"
    - "controlling a triangular flexible formation of autonomous agents"
    - "distributed rotational and translational maneuvering of rigid formations and their applications"

Connected through hyperedge HE-5 (induced by source paper "collaborative target tracking control using multiple autonomous fixed wing uavs with constant speeds theory and experiments"):
  with members:
    - "circular formation control of fixed wing uavs with constant speeds"

Connected through hyperedge HE-6 (induced by source paper "quantization effects and convergence properties of rigid formation control systems with quantized distance measurements"):
  with members:
    - "distributed rotational and translational maneuvering of rigid formations and their applications"

Connected through hyperedge HE-7 (induced by source paper "distributed algorithm for controlling scale free polygonal formations"):
  with members:
    - "distributed rotational and translational maneuvering of rigid formations and their applications"
    - "distributed scaling control of rigid formations"

Connected through hyperedge HE-8 (induced by source paper "controlling a triangular flexible formation of autonomous agents"):
  with members:
    - "distributed rotational and translational maneuvering of rigid formations and their applications"

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)