human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust robotic pouring using audition and haptics"):
  Abstract: Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container.

Includes members:
  Member "making sense of audio vibration for liquid height estimation in robotic pouring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging multimodal haptic sensory data for robust cutting")
  Member "my lips are concealed audio visual speech enhancement through obstructions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mmtm multimodal transfer module for cnn fusion")
  Member "accurate pouring with an autonomous robot using an rgb d camera":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "making sense of audio vibration for liquid height estimation in robotic pouring")
  Member "liquid pouring monitoring via rich sensory inputs":
    also connected through other hyperedges:
      (none)
  Member "a dataset of daily interactive manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tuned inception v3 for recognizing states of cooking ingredients")
  Member "learning to pour":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making sense of audio vibration for liquid height estimation in robotic pouring")
  Member "perceiving and reasoning about liquids using fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "making sense of audio vibration for liquid height estimation in robotic pouring")
  Member "see the glass half full reasoning about liquid containers their volume and content":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning manipulation under physics constraints with visual perception")

Question: Please tell me which class the source paper 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)