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 "core automatic molecule optimization using copy refine strategy"):
  Abstract: Molecule optimization is about generating molecule $Y$ with more desirable properties based on an input molecule $X$. The state-of-the-art approaches partition the molecules into a large set of substructures $S$ and grow the new molecule structure by iteratively predicting which substructure from $S$ to add. However, since the set of available substructures $S$ is large, such an iterative prediction task is often inaccurate especially for substructures that are infrequent in the training data. To address this challenge, we propose a new generating strategy called "Copy & Refine" (CORE), where at each step the generator first decides whether to copy an existing substructure from input $X$ or to generate a new substructure, then the most promising substructure will be added to the new molecule. Combining together with scaffolding tree generation and adversarial training, CORE can significantly improve several latest molecule optimization methods in various measures including drug likeness (QED), dopamine receptor (DRD2) and penalized LogP. We tested CORE and baselines using the ZINC database and CORE obtained up to 11% and 21% relatively improvement over the baselines on success rate on the complete test set and the subset with infrequent substructures, respectively.

Includes members:
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can graph neural networks count substructures")
  Member "constrained graph variational autoencoders for molecule design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "constrained generation of semantically valid graphs via regularizing variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "optimization of molecules via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "deep reinforcement learning for de novo drug design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "junction tree variational autoencoder for molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmarking graph neural networks")
  Member "syntax directed variational autoencoder for structured data":
    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)