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 "graphaf a flow based autoregressive model for molecular graph generation"):
  Abstract: Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep generative models, in this paper we propose a flow-based autoregressive model for graph generation called GraphAF. GraphAF combines the advantages of both autoregressive and flow-based approaches and enjoys: (1) high model flexibility for data density estimation; (2) efficient parallel computation for training; (3) an iterative sampling process, which allows leveraging chemical domain knowledge for valency checking. Experimental results show that GraphAF is able to generate 68% chemically valid molecules even without chemical knowledge rules and 100% valid molecules with chemical rules. The training process of GraphAF is two times faster than the existing state-of-the-art approach GCPN. After fine-tuning the model for goal-directed property optimization with reinforcement learning, GraphAF achieves state-of-the-art performance on both chemical property optimization and constrained property optimization.

Includes members:
  Member "graph normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "molecularrnn generating realistic molecular graphs with optimized properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graphgen a scalable approach to domain agnostic labeled graph generation")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "can graph neural networks count substructures")
  Member "constrained generation of semantically valid graphs via regularizing variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "graphvae towards generation of small graphs using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "automatic chemical design using a data driven continuous representation of molecules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "molecular sets moses a benchmarking platform for molecular generation models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")

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)