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: "learning to smile s"
  Abstract: This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein is considered, which is a crucial part of computer aided drug design process. Conducted experiments show that this way one can not only outrank state of the art results of hand crafted representations but also gets direct structural insights into the way decisions are made.

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 "mol cyclegan a generative model for molecular optimization"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "graph memory networks for molecular activity prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"

Connected through hyperedge HE-3 (induced by source paper "toward explainable anticancer compound sensitivity prediction via multimodal attention based convolutional encoders"):
  with members:
    - "paccmann prediction of anticancer compound sensitivity with multi modal attention based neural networks"
    - "deep reinforcement learning for de novo drug design"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "application of generative autoencoder in de novo molecular design"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-4 (induced by source paper "n gram graph simple unsupervised representation for graphs with applications to molecules"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "restricted isometry property under high correlations"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "how powerful are graph neural networks"
    - "on the dimensionality of word embedding"

Connected through hyperedge HE-5 (induced by source paper "il net using expert knowledge to guide the design of furcated neural networks"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "retrosynthetic reaction prediction using neural sequence to sequence models"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "massive exploration of neural machine translation architectures"
    - "modelling chemical reasoning to predict and invent reactions"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "one shot learning with memory augmented neural networks"
    - "matching networks for one shot learning"
    - "linking the neural machine translation and the prediction of organic chemistry reactions"
    - "low data drug discovery with one shot 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.CL(Computation and Language)