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: "online balanced motion generation for humanoid robots"
  Abstract: Reducing the complexity of higher order problems can enable solving them in analytical ways. In this paper, we propose an analytic whole body motion generator for humanoid robots. Our approach targets inexpensive platforms that possess position controlled joints and have limited feedback capabilities. By analysing the mass distribution in a humanoid-like body, we find relations between limb movement and their respective CoM positions. A full pose of a humanoid robot is then described with five point-masses, with one attached to the trunk and the remaining four assigned to each limb. The weighted sum of these masses in combination with a contact point form an inverted pendulum. We then generate statically stable poses by specifying a desired upright pendulum orientation, and any desired trunk orientation. Limb and trunk placement strategies are utilised to meet the reference CoM position. A set of these poses is interpolated to achieve stable whole body motions. The approach is evaluated by performing several motions with an igus Humanoid Open Platform robot. We demonstrate the extendability of the approach by applying basic feedback mechanisms for disturbance rejection and tracking error minimisation.

Connected through hyperedge HE-1 (induced by source paper "robocup 2019 adultsize winner nimbro deep learning perception in walk kick push recovery and team play capabilities"):
  with members:
    - "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions"
    - "grown up nimbro robots winning robocup 2017 humanoid adultsize soccer competitions"
    - "location dependency in video prediction"
    - "omnidirectional bipedal walking with direct fused angle feedback mechanisms"
    - "a monocular vision system for playing soccer in low color information environments"
    - "real time visual tracking and identification for a team of homogeneous humanoid robots"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"
    - "online visual robot tracking and identification using deep lstm networks"

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)