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: "functional co optimization of articulated robots"
  Abstract: We present parametric trajectory optimization, a method for simultaneously computing physical parameters, actuation requirements, and robot motions for more efficient robot designs. In this scheme, robot dimensions, masses, and other physical parameters are solved for concurrently with traditional motion planning variables, including dynamically consistent robot states, actuation inputs, and contact forces. Our method requires minimal user domain knowledge, requiring only a coarse guess of the target robot configuration sequence and a parameterized robot topology as input. We demonstrate our results on four simulated robots, one of which we physically fabricated in order to demonstrate physical consistency. We demonstrate that by optimizing robot body parameters alongside robot trajectories, motion planning problems which would otherwise be infeasible can be made feasible, and actuation requirements can be significantly reduced.

Connected through hyperedge HE-1 (induced by source paper "globally optimal joint search of topology and trajectory for planar linkages"):
  with members:
    - "two scale topology optimization with microstructures"

Connected through hyperedge HE-2 (induced by source paper "jointly learning to construct and control agents using deep reinforcement learning"):
  with members:
    - "sim to real learning agile locomotion for quadruped robots"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "towards generalization and simplicity in continuous control"
    - "jointly optimizing placement and inference for beacon based localization"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "robot design formalisms representations and the role of the designer"):
  with members:
    - "context aware system synthesis task assignment and routing"

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)