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 "deep learning for molecular design a review of the state of the art"):
  Abstract: In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules—in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

Includes members:
  Member "towards gan benchmarks which require generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair generative modeling via weak supervision")
  Member "adversarial autoencoders with constant curvature latent manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")
  Member "conditional molecular design with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "decoding molecular graph embeddings with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative models for automatic chemical design")
  Member "mol cyclegan a generative model for molecular optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative models for automatic chemical design")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can graph neural networks count substructures")
  Member "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "all smiles variational autoencoder")
  Member "the gan landscape losses architectures regularization and normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)