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: "text2shape generating shapes from natural language by learning joint embeddings"
  Abstract: We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections, and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail. See video at this https URL

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "dynamic graph attention for referring expression comprehension"
    - "embodied language grounding with implicit 3d visual feature representations"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"

Connected through hyperedge HE-3 (induced by source paper "shapecaptioner generative caption network for 3d shapes by learning a mapping from parts detected in multiple views to sentences"):
  with members:
    - "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"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "turbo learning for captionbot and drawingbot"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "cross modal retrieval with implicit concept association"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"

Connected through hyperedge HE-4 (induced by source paper "action2vec a crossmodal embedding approach to action learning"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "learning attributes equals multi source domain generalization"

Connected through hyperedge HE-5 (induced by source paper "let s transfer transformations of shared semantic representations"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "composing text and image for image retrieval an empirical odyssey"
    - "image generation from scene graphs"
    - "dialog based interactive image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "automatic spatially aware fashion concept discovery"
    - "adversarial discriminative domain adaptation"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-6 (induced by source paper "be consistent improving procedural text comprehension using label consistency"):
  with members:
    - "tracking discrete and continuous entity state for process understanding"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "simulating action dynamics with neural process networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "tracking the world state with recurrent entity networks"

Connected through hyperedge HE-7 (induced by source paper "grounding language attributes to objects using bayesian eigenobjects"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "shapenet an information rich 3d model repository"
    - "towards universal paraphrastic sentence embeddings"
    - "natural language object retrieval"
    - "3d shapenets a deep representation for volumetric shapes"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "generation high resolution 3d model from natural language by generative adversarial network"):
  with members:
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

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)