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: "visual closed loop control for pouring liquids"
  Abstract: Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-3 (induced by source paper "end to end robotic reinforcement learning without reward engineering"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "adversarial skill networks unsupervised robot skill learning from video"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "monet unsupervised scene decomposition and representation"
    - "adaptive curriculum generation from demonstrations for sim to real visuomotor control"
    - "hierarchical reinforcement learning for concurrent discovery of compound and composable policies"
    - "unsupervised visuomotor control through distributional planning networks"
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "learning actionable representations from visual observations"

Connected through hyperedge HE-5 (induced by source paper "contextual imagined goals for self supervised robotic learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "reinforcement learning without ground truth state"
    - "self supervised learning of image embedding for continuous control"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "stochastic adversarial video prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"

Connected through hyperedge HE-6 (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"
    - "motion planning for fluid manipulation using simplified dynamics"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (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"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "synthesis of recurrent neural networks for dynamical system simulation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-8 (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"
    - "motion planning for fluid manipulation using simplified dynamics"

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)