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: "predicting organic reaction outcomes with weisfeiler lehman network"
  Abstract: The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and efficiency issues. In this paper, we propose a template-free approach to efficiently explore the space of product molecules by first pinpointing the reaction center -- the set of nodes and edges where graph edits occur. Since only a small number of atoms contribute to reaction center, we can directly enumerate candidate products. The generated candidates are scored by a Weisfeiler-Lehman Difference Network that models high-order interactions between changes occurring at nodes across the molecule. Our framework outperforms the top-performing template-based approach with a 10\% margin, while running orders of magnitude faster. Finally, we demonstrate that the model accuracy rivals the performance of domain experts.

Connected through hyperedge HE-1 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "large scale learnable graph convolutional networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "harp hierarchical representation learning for networks"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "retrosynthesis prediction with conditional graph logic network"):
  with members:
    - "learning retrosynthetic planning through self play"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "deriving neural architectures from sequence and graph kernels"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-3 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-4 (induced by source paper "gnn explainer a tool for post hoc explanation of graph neural networks"):
  with members:
    - "position aware graph neural networks"
    - "explaine an approach for explaining network embedding based link predictions"
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representer point selection for explaining deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph u nets"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "self attention graph pooling"
    - "solving graph compression via optimal transport"
    - "provably powerful graph networks"
    - "online planner selection with graph neural networks and adaptive scheduling"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-6 (induced by source paper "a model to search for synthesizable molecules"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "molecular sets moses a benchmarking platform for molecular generation models"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "evolvegcn evolving graph convolutional networks for dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "graph u nets"
    - "dyngem deep embedding method for dynamic graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "representation learning over dynamic graphs"
    - "attention is all you need"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-8 (induced by source paper "chembo bayesian optimization of small organic molecules with synthesizable recommendations"):
  with members:
    - "a model to search for synthesizable molecules"
    - "conditional molecular design with deep generative models"
    - "optimization of molecules via deep reinforcement learning"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "deriving neural architectures from sequence and graph kernels"

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)