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: "potentialnet for molecular property prediction"
  Abstract: The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. They key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning---instead of feature engineering---deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor $EF_\chi^{(R)}$, to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.

Connected through hyperedge HE-1 (induced by source paper "molecule attention transformer"):
  with members:
    - "alchemy a quantum chemistry dataset for benchmarking ai models"
    - "do attention heads in bert track syntactic dependencies"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "enhanced deep learning prediction of molecular properties via augmentation of bond topology"
    - "self attention based molecule representation for predicting drug target interaction"
    - "what does bert look at an analysis of bert s attention"
    - "adaptive graph convolutional neural networks"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "meta learning initializations for low resource drug discovery"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "pre training graph neural networks"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "latent molecular optimization for targeted therapeutic design"

Connected through hyperedge HE-4 (induced by source paper "analyzing learned molecular representations for property prediction"):
  with members:
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks in molecular graph analysis"
    - "learning multimodal graph to graph translation for molecular optimization"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "pre training graph neural networks with kernels"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "covariant compositional networks for learning graphs"

Connected through hyperedge HE-5 (induced by source paper "sparse hierarchical representation learning on molecular graphs"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "flexible neural representation for physics prediction"
    - "learning graph level representation for drug discovery"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-6 (induced by source paper "uncertainty quantification of molecular property prediction with bayesian neural networks"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "learning deep generative models of graphs"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive biases deep learning and graph networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "atomic convolutional networks for predicting protein ligand binding affinity"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "libmolgrid gpu accelerated molecular gridding for deep learning applications"):
  with members:
    - "optimization of molecules via deep reinforcement learning"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "graph memory networks for molecular activity prediction"
    - "atomic convolutional networks for predicting protein ligand binding affinity"
    - "moleculenet a benchmark for molecular machine learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"

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)