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: "efficient semantic image segmentation with superpixel pooling"
  Abstract: In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at this https URL

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"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "meshdepth disconnected mesh based deep depth prediction"):
  with members:
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "deeptam deep tracking and mapping"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-3 (induced by source paper "recognizing image objects by relational analysis using heterogeneous superpixels and deep convolutional features"):
  with members:
    - "deformable convnets v2 more deformable better results"
    - "dynamic routing between capsules"
    - "understanding convolution for semantic segmentation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "u net convolutional networks for biomedical image segmentation"
    - "gslicr slic superpixels at over 250hz"
    - "very deep convolutional networks for large scale image recognition"
    - "feedforward semantic segmentation with zoom out features"

Connected through hyperedge HE-4 (induced by source paper "generating superpixels using deep image representations"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "scaling the scattering transform deep hybrid networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semi supervised classification with graph convolutional networks"

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)