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: "text to 3d scene generation with rich lexical grounding"
  Abstract: The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them. We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects. Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods. We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments. To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments.

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 "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction"):
  with members:
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "fast and scalable position based layout synthesis"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "grains generative recursive autoencoders for indoor scenes"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "automatic generation of constrained furniture layouts"

Connected through hyperedge HE-3 (induced by source paper "layoutvae stochastic scene layout generation from a label set"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "grains generative recursive autoencoders for indoor scenes"

Connected through hyperedge HE-4 (induced by source paper "image generation from scene graphs"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "improved image captioning via policy gradient optimization of spider"
    - "scene graph generation by iterative message passing"
    - "on support relations and semantic scene graphs"
    - "pixels to graphs by associative embedding"
    - "photographic image synthesis with cascaded refinement networks"
    - "premise selection for theorem proving by deep graph embedding"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised learning of visual relations"):
  with members:
    - "learning from video and text via large scale discriminative clustering"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "a multipath network for object detection"
    - "visual relationship detection with language priors"
    - "captioning images with diverse objects"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-6 (induced by source paper "person re identification with vision and language"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-7 (induced by source paper "multimodal semantic simulations of linguistically underspecified motion events"):
  with members:
    - "fine grained event learning of human object interaction with lstm crf"
    - "voxml a visualization modeling language"
    - "ecat event capture annotation tool"
    - "globally normalized transition based neural networks"
    - "generating simulations of motion events from verbal descriptions"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

Connected through hyperedge HE-8 (induced by source paper "voxml a visualization modeling language"):
  with members:
    - "generating simulations of motion events from verbal descriptions"
    - "international standard for a linguistic annotation framework"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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.CL(Computation and Language)