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: "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).

Connected through hyperedge HE-1 (induced by source paper "continuous graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "diffusion improves graph learning"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "discrete and continuous deep residual learning over graphs"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"

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)