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: "attribute2image conditional image generation from visual attributes"
  Abstract: This paper investigates a problem of generating images from visual attributes. Given the prevalent research for image recognition, the conditional image generation problem is relatively under-explored due to the challenges of learning a good generative model and handling rendering uncertainties in images. To address this, we propose a variety of attribute-conditioned deep variational auto-encoders that enjoy both effective representation learning and Bayesian modeling, from which images can be generated from specified attributes and sampled latent factors. We experiment with natural face images and demonstrate that the proposed models are capable of generating realistic faces with diverse appearance. We further evaluate the proposed models by performing attribute-conditioned image progression, transfer and retrieval. In particular, our generation method achieves superior performance in the retrieval experiment against traditional nearest-neighbor-based methods both qualitatively and quantitatively.

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 "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-3 (induced by source paper "adversarial code learning for image generation"):
  with members:
    - "image transformer"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "deep bayesian network for visual question generation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "creativity generating diverse questions using variational autoencoders"
    - "what s in a question using visual questions as a form of supervision"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-7 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings"):
  with members:
    - "lpaintb learning to paint from self supervision"
    - "visual deprojection probabilistic recovery of collapsed dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "context aware synthesis for video frame interpolation"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to generate long term future via hierarchical prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "latent constraints learning to generate conditionally from unconditional generative models"

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.LG(Machine Learning)