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: "detection and tracking of liquids with fully convolutional networks"
  Abstract: Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames, in contrast to standard image segmentation. They also show that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.

Connected through hyperedge HE-1 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation"):
  with members:
    - "affordance learning for end to end visuomotor robot control"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "invisible marker automatic annotation for object manipulation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "virtual to real learning to control in visual semantic segmentation"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "matterport3d learning from rgb d data in indoor environments"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "liquid pouring monitoring via rich sensory inputs"):
  with members:
    - "joint discovery of object states and manipulation actions"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "time contrastive networks self supervised learning from video"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "egocentric activity recognition with multimodal fisher vector"
    - "visual closed loop control for pouring liquids"

Connected through hyperedge HE-4 (induced by source paper "accurate pouring with an autonomous robot using an rgb d camera"):
  with members:
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "motion planning for fluid manipulation using simplified dynamics"
    - "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.CV(Computer Vision and Pattern Recognition)