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: "state estimation for a humanoid robot"
  Abstract: This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid’s flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

Connected through hyperedge HE-1 (induced by source paper "contact aided invariant extended kalman filtering for legged robot state estimation"):
  with members:
    - "rins w robust inertial navigation system on wheels"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "robocentric visual inertial odometry"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "invariant smoothing on lie groups"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"):
  with members:
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "legged robot state estimation through combined forward kinematic and preintegrated contact factors"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-3 (induced by source paper "unsupervised contact learning for humanoid estimation and control"):
  with members:
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "walking on partial footholds including line contacts with the humanoid robot atlas"

Connected through hyperedge HE-4 (induced by source paper "legged robot state estimation through combined forward kinematic and preintegrated contact factors"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"):
  with members:
    - "an efficiently solvable quadratic program for stabilizing dynamic locomotion"
    - "balancing experiments on a torque controlled humanoid with hierarchical inverse dynamics"

Connected through hyperedge HE-6 (induced by source paper "humanoid momentum estimation using sensed contact wrenches"):
  with members:
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "trajectory generation for multi contact momentum control"

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)