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 "tabulated mlp for fast point feature embedding"):
  Abstract: Aiming at a drastic speedup for point-data embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptron (MLP) and look-up table (LUT) to transform point-coordinate inputs into high-dimensional features. When compared with PointNet's feature embedding part realized by MLP that requires millions of dot products, ours at test time requires no such layers of matrix-vector products but requires only looking up the nearest entities followed by interpolation, from the tabulated MLP defined over discrete inputs on a 3D lattice. We call this framework as "LUTI-MLP: LUT Interpolation MLP" that provides a way to train end-to-end tabulated MLP coupled to a LUT in a specific manner without the need for any approximation at test time. LUTI-MLP also provides significant speedup for Jacobian computation of the embedding function wrt global pose coordinate on Lie algebra $\mathfrak{se}(3)$ at test time, which could be used for point-set registration problems. After extensive architectural analysis using ModelNet40 dataset, we confirmed that our LUTI-MLP even with a small-sized table ($8\times 8\times 8$) yields performance comparable to that of MLP while achieving significant speedup: $80\times$ for embedding, $12\times$ for approximate Jacobian, and $860\times$ for canonical Jacobian.

Includes members:
  Member "deepicp an end to end deep neural network for 3d point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "pcrnet point cloud registration network using pointnet encoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "samplenet differentiable point cloud sampling")
  Member "pointnetlk robust efficient point cloud registration using pointnet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "deep closest point learning representations for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "prnet self supervised learning for partial to partial registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "pointpillars fast encoders for object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review deep learning on 3d point clouds")
  Member "splatnet sparse lattice networks for point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

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)