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: "learning to pour"
  Abstract: Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics.

Connected through hyperedge HE-1 (induced by source paper "robust robotic pouring using audition and haptics"):
  with members:
    - "making sense of audio vibration for liquid height estimation in robotic pouring"
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "liquid pouring monitoring via rich sensory inputs"
    - "a dataset of daily interactive manipulation"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "motion planning for fluid manipulation using simplified dynamics"
    - "visual closed loop control for pouring liquids"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "accurate robotic pouring for serving drinks"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"
    - "functional object oriented network for manipulation learning"
    - "visual closed loop control for pouring liquids"
    - "synthesis of recurrent neural networks for dynamical system simulation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "ai meets physical world exploring robot cooking"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "a dataset of daily interactive manipulation"
    - "functional object oriented network for manipulation learning"
    - "datasets on object manipulation and interaction a survey"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a dataset of daily interactive manipulation"):
  with members:
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"
    - "latest datasets and technologies presented in the workshop on grasping and manipulation datasets"
    - "datasets on object manipulation and interaction a survey"

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)