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 "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.

Includes members:
  Member "to fall or not to fall a visual approach to physical stability prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "visual closed loop control for pouring liquids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust robotic pouring using audition and haptics")
  Member "learning to poke by poking experiential learning of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "learning physical intuition of block towers by example":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "what happens if learning to predict the effect of forces in images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "belief regulated dual propagation nets for learning action effects on groups of articulated objects")
  Member "are elephants bigger than butterflies reasoning about sizes of objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "olmpics on what language model pre training captures")
  Member "a multipath network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "har net joint learning of hybrid attention for single stage object detection")
  Member "newtonian image understanding unfolding the dynamics of objects in static images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting the physical dynamics of unseen 3d objects")

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)