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: "see the glass half full reasoning about liquid containers their volume and content"
  Abstract: Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher. Very little attention in computer vision has been made to liquids and their containers. In this paper, we study liquid containers and their contents, and propose methods to estimate the volume of containers, approximate the amount of liquid in them, and perform comparative volume estimations all from a single RGB image. Furthermore, we show the results of the proposed model for predicting the behavior of liquids inside containers when one tilts the containers. We also introduce a new dataset of Containers Of liQuid contEnt (COQE) that contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models.

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"
    - "reasoning about liquids via closed loop simulation"

Connected through hyperedge HE-2 (induced by source paper "learning manipulation under physics constraints with visual perception"):
  with members:
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "learning model based planning from scratch"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "metacontrol for adaptive imagination based optimization"
    - "imagination augmented agents for deep reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "long term image boundary extrapolation"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "visual affordance and function understanding a survey"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "geometric affordances from a single example via the interaction tensor"
    - "predicting human activities using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "deep affordance grounded sensorimotor object recognition"
    - "transfer of tool affordance and manipulation cues with 3d vision data"
    - "adaptive binarization for weakly supervised affordance segmentation"
    - "learning to act properly predicting and explaining affordances from images"

Connected through hyperedge HE-5 (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"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "egocentric activity recognition with multimodal fisher vector"
    - "detection and tracking of liquids with fully convolutional networks"
    - "visual closed loop control for pouring liquids"

Connected through hyperedge HE-6 (induced by source paper "acquiring target stacking skills by goal parameterized deep reinforcement learning"):
  with members:
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "long term image boundary prediction"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "learning visual predictive models of physics for playing billiards"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-7 (induced by source paper "accurate pouring with an autonomous robot using an rgb d camera"):
  with members:
    - "motion planning for fluid manipulation using simplified dynamics"
    - "detection and tracking of liquids with fully convolutional networks"
    - "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)