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: "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.

Connected through hyperedge HE-1 (induced by source paper "selfies a robust representation of semantically constrained graphs with an example application in chemistry"):
  with members:
    - "interpretable deep learning in drug discovery"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep reinforcement learning for de novo drug design"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning deep generative models of graphs"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "autoencoding undirected molecular graphs with neural networks"):
  with members:
    - "self attention with relative position representations"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "gen highly efficient smiles explorer using autodidactic generative examination networks"):
  with members:
    - "from gan to wgan"
    - "a survey on deep transfer learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "molecular generation with recurrent neural networks rnns"
    - "recent trends in deep learning based natural language processing"
    - "in silico generation of novel drug like chemical matter using the lstm neural network"

Connected through hyperedge HE-4 (induced by source paper "distance preserving graph embeddings from random neural features"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "generative adversarial networks gan based efficient sampling of chemical space for inverse design of inorganic materials"):
  with members:
    - "continuous dice coefficient a method for evaluating probabilistic segmentations"
    - "conditional molecular design with deep generative models"
    - "data driven approach to encoding and decoding 3 d crystal structures"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "discovering molecular functional groups using graph convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained graph variational autoencoders for molecule design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "smoothgrad removing noise by adding noise"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "densely connected convolutional networks"

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)