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: "motion planning for fluid manipulation using simplified dynamics"
  Abstract: We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically inspired approximations and system identification. Our optimization approach can incorporate various other constraints such as collision avoidance with the obstacles, kinematic and dynamics constraints of the manipulator, and fluid dynamics characteristics. We demonstrate the performance of our planner on different benchmarks corresponding to various obstacles and container shapes. Furthermore, we also evaluate its accuracy by validating the motion plan using an accurate but computationally costly Navier-Stokes fluid simulation.

Connected through hyperedge HE-1 (induced by source paper "making sense of audio vibration for liquid height estimation in robotic pouring"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "learning to pour"
    - "perceiving and reasoning about liquids using fully convolutional networks"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "reasoning about liquids via closed loop simulation"
    - "visual closed loop control for pouring liquids"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "to stir or not to stir online estimation of liquid properties for pouring actions"):
  with members:
    - "reasoning about liquids via closed loop simulation"
    - "visual closed loop control for pouring liquids"

Connected through hyperedge HE-3 (induced by source paper "accurate pouring with an autonomous robot using an rgb d camera"):
  with members:
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "detection and tracking of liquids with fully convolutional networks"
    - "visual closed loop control for pouring liquids"

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)