human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "continuous graph flow for flexible density estimation"):
  Abstract: In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model distributions of graph-structured complex data. The model is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graph structure. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time. This class of models offer several advantages: (1) modeling complex graphical distributions without rigid assumptions on the distributions; (2) not limited to modeling data of fixed dimensions and can generalize probability evaluation and data generation over unseen subset of variables; (3) the underlying continuous graph message passing process is reversible and memory-efficient. We demonstrate the effectiveness of our model on two generation tasks, namely, image puzzle generation, and layout generation from scene graphs. Compared to unstructured and structured latent-space VAE models, we show that our proposed model achieves significant performance improvement (up to 400% in negative log-likelihood).

Includes members:
  Member "layoutvae stochastic scene layout generation from a label set":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "house gan relational generative adversarial networks for graph constrained house layout generation")
  Member "graphvae towards generation of small graphs using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "exploiting semantics in neural machine translation with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a gentle introduction to deep learning for graphs")
  Member "graph to sequence learning using gated graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "knowledge base completion with out of knowledge base entities a graph neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic latent residual video prediction")
  Member "ffjord free form continuous dynamics for scalable reversible generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "image generation from scene graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

Question: Please tell me which class the source paper 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)