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: "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"
  Abstract: Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architecture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target information successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark.

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"
    - "potentialnet for molecular property prediction"

Connected through hyperedge HE-2 (induced by source paper "combining machine learning with knowledge based modeling for scalable forecasting and subgrid scale closure of large complex spatiotemporal systems"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "efficient design of hardware enabled reservoir computing in fpgas"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-4 (induced by source paper "kekulescope prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images"):
  with members:
    - "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"
    - "hydranet data augmentation for regression neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "improving deep learning using generic data augmentation"
    - "molecular de novo design through deep reinforcement learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-5 (induced by source paper "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "on calibration of modern neural networks"
    - "molecular de novo design through deep reinforcement learning"
    - "confidence estimation in deep neural networks via density modelling"
    - "exploring loss function topology with cyclical learning rates"
    - "low data drug discovery with one shot learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "towards open set deep networks"

Connected through hyperedge HE-6 (induced by source paper "enhanced deep learning prediction of molecular properties via augmentation of bond topology"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "how powerful are graph neural networks"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "modelling chemical reasoning to predict and invent reactions"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "landscape of big medical data a pragmatic survey on prioritized tasks"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep activity recognition models with triaxial accelerometers"

Connected through hyperedge HE-8 (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"
    - "atomic convolutional networks for predicting protein ligand binding affinity"
    - "moleculenet a benchmark for molecular machine learning"

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)