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 "composing molecules with multiple property constraints"):
  Abstract: Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset this complexity by composing molecules from a vocabulary of substructures that we call molecular rationales. These rationales are identified from molecules as substructures that are likely responsible for each property of interest. We then learn to expand rationales into a full molecule using graph generative models. Our final generative model composes molecules as mixtures of multiple rationale completions, and this mixture is fine-tuned to preserve the properties of interest. We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds.

Includes members:
  Member "functional transparency for structured data a game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach")
  Member "discrete object generation with reversible inductive construction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  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 "gnnexplainer generating explanations for graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entity context and relational paths for knowledge graph completion")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "molecular hypergraph grammar with its application to molecular optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypergraphs an introduction and review")
  Member "constrained generation of semantically valid graphs via regularizing variational autoencoders":
    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)