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: "beam search for learning a deep convolutional neural network of 3d shapes"
  Abstract: This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape, and resorts to deep convolutional neural networks(CNNs) for modeling these binary variables. Robust learning of such CNNs is currently limited by the small datasets of 3D shapes available, an order of magnitude smaller than other common datasets in computer vision. Related work typically deals with the small training datasets using a number of ad hoc, hand-tuning strategies. To address this issue, we formulate CNN learning as a beam search aimed at identifying an optimal CNN architecture, namely, the number of layers, nodes, and their connectivity in the network, as well as estimating parameters of such an optimal CNN. Each state of the beam search corresponds to a candidate CNN. Two types of actions are defined to add new convolutional filters or new convolutional layers to a parent CNN, and thus transition to children states. The utility function of each action is efficiently computed by transferring parameter values of the parent CNN to its children, thereby enabling an efficient beam search. Our experimental evaluation on the 3D ModelNet dataset demonstrates that our model pursuit using the beam search yields a CNN with superior performance on 3D shape classification than the state of the art.

Connected through hyperedge HE-1 (induced by source paper "lp 3dcnn unveiling local phase in 3d convolutional neural networks"):
  with members:
    - "modelling local phase of images and textures with applications in phase denoising and phase retrieval"
    - "spatio temporal channel correlation networks for action classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-2 (induced by source paper "mv c3d a spatial correlated multi view 3d convolutional neural networks"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "so net self organizing network for point cloud analysis"
    - "angular triplet center loss for multi view 3d shape retrieval"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "exploring temporal differences in 3d convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "motion feature network fixed motion filter for action recognition"
    - "spatio temporal channel correlation networks for action classification"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convnet architecture search for spatiotemporal feature learning"

Connected through hyperedge HE-4 (induced by source paper "orthographicnet a deep learning approach for 3d object recognition in open ended domains"):
  with members:
    - "low shot learning from imaginary data"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "learning transferable architectures for scalable image recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "densely connected convolutional networks"
    - "group equivariant convolutional networks"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-5 (induced by source paper "cubenet equivariance to 3d rotation and translation"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"

Connected through hyperedge HE-6 (induced by source paper "multi level 3d cnn for learning multi scale spatial features"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-7 (induced by source paper "3d object classification via spherical projections"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "orientation boosted voxel nets for 3d object recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "shapenet an information rich 3d model repository"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-8 (induced by source paper "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "unsupervised learning of depth and ego motion from video"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "learning 3d object categories by looking around them"
    - "orientation boosted voxel nets for 3d object recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "fusionnet 3d object classification using multiple data representations"

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)