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 "what can i do here leveraging deep 3d saliency and geometry for fast and scalable multiple affordance detection"):
  Abstract: This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds. Our approach has many advantages over alternative methods, as it is based on highly parallelizable, one-shot learning that is fast in commodity hardware. The approach is hybrid in that it uses a geometric representation together with a state-of-the-art deep learning method capable of identifying 3D scene saliency. The geometric component allows for a compact and efficient representation, boosting the performance of the deep network architecture which proved insufficient on its own. Moreover, our approach allows not only to predict whether an input scene affords or not the interactions, but also the pose of the objects that allow these interactions to take place. Our predictions align well with crowd-sourced human judgment as they are preferred with 87% probability, show high rates of improvement with almost four times (4x) better performance over a deep learning-only baseline and are seven times (7x) faster than previous art.

Includes members:
  Member "escape from cells deep kd networks for the recognition of 3d point cloud models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "learning to act properly predicting and explaining affordances from images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what object should i use task driven object detection")
  Member "affordancenet an end to end deep learning approach for object affordance detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "generative and discriminative voxel modeling with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "octnet learning deep 3d representations at high resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pointnet deep learning on point sets for 3d classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "fpnn field probing neural networks for 3d data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)