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 "liquid pouring monitoring via rich sensory inputs"):
  Abstract: Humans have the amazing ability to perform very subtle manipulation task using a closed-loop control system with imprecise mechanics (i.e., our body parts) but rich sensory information (e.g., vision, tactile, etc.). In the closed-loop system, the ability to monitor the state of the task via rich sensory information is important but often less studied. In this work, we take liquid pouring as a concrete example and aim at learning to continuously monitor whether liquid pouring is successful (e.g., no spilling) or not via rich sensory inputs. We mimic humans' rich sensories using synchronized observation from a chest-mounted camera and a wrist-mounted IMU sensor. Given many success and failure demonstrations of liquid pouring, we train a hierarchical LSTM with late fusion for monitoring. To improve the robustness of the system, we propose two auxiliary tasks during training: inferring (1) the initial state of containers and (2) forecasting the one-step future 3D trajectory of the hand with an adversarial training procedure. These tasks encourage our method to learn representation sensitive to container states and how objects are manipulated in 3D. With these novel components, our method achieves ~8% and ~11% better monitoring accuracy than the baseline method without auxiliary tasks on unseen containers and unseen users respectively.

Includes members:
  Member "joint discovery of object states and manipulation actions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "a closer look at spatiotemporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "time contrastive networks self supervised learning from video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "ava a video dataset of spatio temporally localized atomic visual actions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "see the glass half full reasoning about liquid containers their volume and content":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust robotic pouring using audition and haptics")
  Member "optical flow guided feature a fast and robust motion representation for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "v4d 4d convolutional neural networks for video level representation learning")
  Member "egocentric activity recognition with multimodal fisher vector":
    also connected through other hyperedges:
      (none)
  Member "detection and tracking of liquids with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation")

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.CV(Computer Vision and Pattern Recognition)