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: "identification of fully physical consistent inertial parameters using optimization on manifolds"
  Abstract: This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.

Connected through hyperedge HE-1 (induced by source paper "encoding physical constraints in differentiable newton euler algorithm"):
  with members:
    - "a general framework for structured learning of mechanical systems"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"
    - "a new data source for inverse dynamics learning"

Connected through hyperedge HE-2 (induced by source paper "gaussians on riemannian manifolds for robot learning and adaptive control"):
  with members:
    - "rmpflow a computational graph for automatic motion policy generation"
    - "the vector heat method"
    - "geometry aware manipulability learning tracking and transfer"
    - "a micro lie theory for state estimation in robotics"
    - "on manifold preintegration for real time visual inertial odometry"
    - "trajectory synthesis for fisher information maximization"

Connected through hyperedge HE-3 (induced by source paper "a convex optimization based dynamic model identification package for the da vinci research kit"):
  with members:
    - "open sourced reinforcement learning environments for surgical robotics"
    - "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"

Connected through hyperedge HE-4 (induced by source paper "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"):
  with members:
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

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)