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: "pointwise an unsupervised point wise feature learning network"
  Abstract: We present a novel approach to learning a point-wise, meaningful embedding for point-clouds in an unsupervised manner, through the use of neural-networks. The domain of point-cloud processing via neural-networks is rapidly evolving, with novel architectures and applications frequently emerging. Within this field of research, the availability and plethora of unlabeled point-clouds as well as their possible applications make finding ways of characterizing this type of data appealing. Though significant advancement was achieved in the realm of unsupervised learning, its adaptation to the point-cloud representation is not trivial. Previous research focuses on the embedding of entire point-clouds representing an object in a meaningful manner. We present a deep learning framework to learn point-wise description from a set of shapes without supervision. Our approach leverages self-supervision to define a relevant loss function to learn rich per-point features. We train a neural-network with objectives based on context derived directly from the raw data, with no added annotation. We use local structures of point-clouds to incorporate geometric information into each point's latent representation. In addition to using local geometric information, we encourage adjacent points to have similar representations and vice-versa, creating a smoother, more descriptive representation. We demonstrate the ability of our method to capture meaningful point-wise features through three applications. By clustering the learned embedding space, we perform unsupervised part-segmentation on point clouds. By calculating euclidean distance in the latent space we derive semantic point-analogies. Finally, by retrieving nearest-neighbors in our learned latent space we present meaningful point-correspondence within and among point-clouds.

Connected through hyperedge HE-1 (induced by source paper "hypernetwork approach to generating point clouds"):
  with members:
    - "conditional invertible flow for point cloud generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pixor real time 3d object detection from point clouds"
    - "hypernetwork functional image representation"
    - "multiresolution tree networks for 3d point cloud processing"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "patch based progressive 3d point set upsampling"

Connected through hyperedge HE-2 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "waveglow a flow based generative network for speech synthesis"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"):
  with members:
    - "neuralsampler euclidean point cloud auto encoder and sampler"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "multiresolution tree networks for 3d point cloud processing"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

Connected through hyperedge HE-4 (induced by source paper "dar net dynamic aggregation network for semantic scene segmentation"):
  with members:
    - "mortonnet self supervised learning of local features in 3d point clouds"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "adversarial autoencoders for compact representations of 3d point clouds"):
  with members:
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "graph based over segmentation methods for 3d point clouds"
    - "training triplet networks with gan"
    - "semantic visual localization"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"

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)