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: "atomic convolutional networks for predicting protein ligand binding affinity"
  Abstract: Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction.

Connected through hyperedge HE-1 (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"
    - "potentialnet for molecular property prediction"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "libmolgrid gpu accelerated molecular gridding for deep learning applications"):
  with members:
    - "potentialnet for molecular property prediction"
    - "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"
    - "moleculenet a benchmark for molecular machine learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "drug target indication prediction by integrating end to end learning and fingerprints"):
  with members:
    - "padme a deep learning based framework for drug target interaction prediction"
    - "multimodal machine learning a survey and taxonomy"
    - "moleculenet a benchmark for molecular machine learning"
    - "low data drug discovery with one shot learning"
    - "unsupervised learning of visual structure using predictive generative networks"

Connected through hyperedge HE-4 (induced by source paper "predicting drug target interaction using 3d structure embedded graph representations from graph neural networks"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "neural message passing for quantum chemistry"
    - "interaction networks for learning about objects relations and physics"

Connected through hyperedge HE-5 (induced by source paper "molecular sets moses a benchmarking platform for molecular generation models"):
  with members:
    - "conditional molecular design with deep generative models"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "deep reinforcement learning for de novo drug design"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"

Connected through hyperedge HE-6 (induced by source paper "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"):
  with members:
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "adaptive graph convolutional neural networks"):
  with members:
    - "deep learning with dynamic computation graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "moleculenet a benchmark for molecular machine learning"
    - "semi supervised classification with graph convolutional networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "low data drug discovery with one shot learning"
    - "deep convolutional networks on graph structured data"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "potentialnet for molecular property prediction"):
  with members:
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"
    - "the numpy array a structure for efficient numerical computation"

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)