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 "recurrent slice networks for 3d segmentation on point clouds"):
  Abstract: In this paper, we present a conceptually simple and powerful framework, Recurrent Slice Network (RSNet), for 3D semantic segmentation on point clouds. Performing 3D segmentation on point clouds is computationally efficient. And it is free of the quantitation artifact problems which exists in other 3D data formats such as voxelized volumes and multi view renderings. However, existing point clouds based methods either do not model local dependencies or rely on heavy extra computations. In contrast, our RSNet is equipped with a lightweight local dependency module, which is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice unpooling layer. The slice pooling layer is designed to project features of unordered points into an ordered sequence of feature vectors. Then, RNNs are applied to model dependencies for the sequence. We validate the importance of local contexts and the effectiveness of our RSNet on the S3DIS, ScanNet, and ShapeNet dataset. Without bells and whistles, RSNet surpasses all previous state-of-the-art methods on these benchmarks. Moreover, additional computation analysis demonstrates the efficiency of RSNet.

Includes members:
  Member "scannet richly annotated 3d reconstructions of indoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review on deep learning techniques for 3d sensed data classification")
  Member "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "sparse 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision")
  Member "spatially sparse convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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)