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 "generative models for automatic chemical design"):
  Abstract: Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.

Includes members:
  Member "selfies a robust representation of semantically constrained graphs with an example application in chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph deconvolutional generation")
  Member "a model to search for synthesizable molecules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a gentle introduction to deep learning for graphs")
  Member "all smiles variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection")
  Member "likelihood free inference and generation of molecular graphs":
    also connected through other hyperedges:
      (none)
  Member "molecular geometry prediction using a deep generative graph neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the discrepancy between density estimation and sequence generation")
  Member "labeled graph generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graphgen a scalable approach to domain agnostic labeled graph generation")
  Member "decoding molecular graph embeddings with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for molecular design a review of the state of the art")
  Member "scaffold based molecular design using graph generative model":
    also connected through other hyperedges:
      (none)

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)