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: "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
  Abstract: This paper introduces a novel approach for 3D semantic instance segmentation on point clouds. A 3D convolutional neural network called submanifold sparse convolutional network is used to generate semantic predictions and instance embeddings simultaneously. To obtain discriminative embeddings for each 3D instance, a structure-aware loss function is proposed which considers both the structure information and the embedding information. To get more consistent embeddings for each 3D instance, attention-based k nearest neighbour (KNN) is proposed to assign different weights for different neighbours. Based on the attention-based KNN, we add a graph convolutional network after the sparse convolutional network to get refined embeddings. Our network can be trained end-to-end. A simple mean-shift algorithm is utilized to cluster refined embeddings to get final instance predictions. As a result, our framework can output both the semantic prediction and the instance prediction. Experiments show that our approach outperforms all state-of-art methods on ScanNet benchmark and NYUv2 dataset.

Connected through hyperedge HE-1 (induced by source paper "occuseg occupancy aware 3d instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "upsnet a unified panoptic segmentation network"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "deep hough voting for 3d object detection in point clouds"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

Connected through hyperedge HE-3 (induced by source paper "3d instance segmentation via multi task metric learning"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "semi convolutional operators for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction"):
  with members:
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "on the convergence of adam and beyond"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "recurrent pixel embedding for instance grouping"

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)