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 reinforcement learning for de novo drug design"
  Abstract: We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks—generative and predictive—that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo–generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.

Connected through hyperedge HE-1 (induced by source paper "hierarchical generation of molecular graphs using structural motifs"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "graph u nets"
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "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"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (induced by source paper "composing molecules with multiple property constraints"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "discrete object generation with reversible inductive construction"
    - "conditional molecular design with deep generative models"
    - "gnnexplainer generating explanations for graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "improving molecular design by stochastic iterative target augmentation"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "conditional molecular design with deep generative models"
    - "pre training graph neural networks"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "design by adaptive sampling"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-5 (induced by source paper "deep learning for molecular design a review of the state of the art"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "conditional molecular design with deep generative models"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "mol cyclegan a generative model for molecular optimization"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "the gan landscape losses architectures regularization and normalization"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning applications"):
  with members:
    - "coloring big graphs with alphagozero"
    - "learning agile and dynamic motor skills for legged robots"
    - "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-8 (induced by source paper "all smiles variational autoencoder"):
  with members:
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "potentialnet for molecular property prediction"
    - "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"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"

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.AI(Artificial Intelligence)