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: "perceiving and reasoning about liquids using fully convolutional networks"
  Abstract: Liquids are an important part of many common manipulation tasks in human environments. If we wish to have robots that can accomplish these types of tasks, they must be able to interact with liquids in an intelligent manner. In this paper, we investigate ways for robots to perceive and reason about liquids. That is, a robot asks the questions What in the visual data stream is liquid? and How can I use that to infer all the potential places where liquid might be? We collected two datasets to evaluate these questions, one using a realistic liquid simulator and another on our robot. We used fully convolutional neural networks to learn to detect and track liquids across pouring sequences. Our results show that these networks are able to perceive and reason about liquids, and that integrating temporal information is important to performing such tasks well.

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"
    - "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"
    - "learning to pour"
    - "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 "color coded fiber optic tactile sensor for an elastomeric robot skin"):
  with members:
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "robotic tactile perception of object properties a review"

Connected through hyperedge HE-4 (induced by source paper "spnets differentiable fluid dynamics for deep neural networks"):
  with members:
    - "universal planning networks"
    - "splatnet sparse lattice networks for point cloud processing"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "visual interaction networks"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "reasoning about liquids via closed loop simulation"
    - "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)