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 "hierarchical generation of molecular graphs using structural motifs"):
  Abstract: Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.

Includes members:
  Member "discrete object generation with reversible inductive construction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "composing molecules with multiple property constraints")
  Member "graph u nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "conditional molecular design with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "composing molecules with multiple property constraints")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "constrained generation of semantically valid graphs via regularizing variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "graphvae towards generation of small graphs using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memory based graph networks")

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)