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: "p2p net bidirectional point displacement net for shape transform"
  Abstract: We introduce P2P-NET, a general-purpose deep neural network that learns geometric transformations between point-based shape representations from two domains (e.g., meso-skeletons and surfaces, partial and complete scans, etc.).

Connected through hyperedge HE-1 (induced by source paper "multimodal shape completion via conditional generative adversarial networks"):
  with members:
    - "morphing and sampling network for dense point cloud completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "dense 3d object reconstruction from a single depth view"
    - "unpaired point cloud completion on real scans using adversarial training"
    - "visual object networks image generation with disentangled 3d representation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-2 (induced by source paper "densepoint learning densely contextual representation for efficient point cloud processing"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "adaptive graph convolutional neural networks"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-3 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deepflux for skeletons in the wild"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-4 (induced by source paper "a convolutional decoder for point clouds using adaptive instance normalization"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "on the convergence of adam and beyond"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point convolutional neural networks by extension operators"
    - "learning implicit fields for generative shape modeling"
    - "so net self organizing network for point cloud analysis"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-5 (induced by source paper "tranquil clouds neural networks for learning temporally coherent features in point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "ursa a neural network for unordered point clouds using constellations"
    - "a graph cnn for 3d point cloud classification"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-6 (induced by source paper "sdm net deep generative network for structured deformable mesh"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-7 (induced by source paper "going deeper with point networks"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "multiresolution tree networks for 3d point cloud processing"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-8 (induced by source paper "logan unpaired shape transform in latent overcomplete space"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "point convolutional neural networks by extension operators"
    - "multimodal unsupervised image to image translation"
    - "nam non adversarial unsupervised domain mapping"
    - "pcpnet learning local shape properties from raw point clouds"
    - "drit diverse image to image translation via disentangled 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.GR(Graphics)