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: "newton methods for k order markov constrained motion problems"
  Abstract: This is a documentation of a framework for robot motion optimization that aims to draw on classical constrained optimization methods. With one exception the underlying algorithms are classical ones: Gauss-Newton (with adaptive step size and damping), Augmented Lagrangian, log-barrier, etc. The exception is a novel any-time version of the Augmented Lagrangian. The contribution of this framework is to frame motion optimization problems in a way that makes the application of these methods efficient, especially by defining a very general class of robot motion problems while at the same time introducing abstractions that directly reflect the API of the source code.

Connected through hyperedge HE-1 (induced by source paper "prediction of human full body movements with motion optimization and recurrent neural networks"):
  with members:
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "modeling human motion with quaternion based neural networks"
    - "learning human motion models for long term predictions"
    - "on human motion prediction using recurrent neural networks"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"

Connected through hyperedge HE-2 (induced by source paper "trajectory optimization for coordinated human robot collaboration"):
  with members:
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "on the fundamental importance of gauss newton in motion optimization"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (induced by source paper "riemannian motion policies"):
  with members:
    - "approximately optimal continuous time motion planning and control via probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "a novel skill based programming paradigm based on autonomous playing and skill centric testing"):
  with members:
    - "skill learning by autonomous robotic playing using active learning and creativity"
    - "robotic playing for hierarchical complex skill learning"

Connected through hyperedge HE-5 (induced by source paper "approximately optimal continuous time motion planning and control via probabilistic inference"):
  with members:
    - "functional gradient motion planning in reproducing kernel hilbert spaces"

Connected through hyperedge HE-6 (induced by source paper "physical problem solving joint planning with symbolic geometric and dynamic constraints"):
  with members:
    - "the predictron end to end learning and planning"

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)