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: "deepcci end to end deep learning for chemical chemical interaction prediction"
  Abstract: Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic effects, and biological functions. CCI was created from text mining, experiments, similarities, and databases; to date, no learning-based CCI prediction method exist. In chemical analyses, computational approaches are required. The recent remarkable growth and outstanding performance of deep learning have attracted considerable esearch attention. However,even in state-of-the-art drug analyses, deep learning continues to be used only as a classifier. Nevertheless, its purpose includes not only simple classification, but also automated feature extraction. In this paper, we propose the first end-to-end learning method for CCI, named DeepCCI. Hidden features are derived from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure, instead of learning from crafted features. To discover hidden representations for the SMILES strings, we use convolutional neural networks (CNNs). To guarantee the commutative property for homogeneous interaction, we apply model sharing and hidden representation merging techniques. The performance of DeepCCI was compared with a plain deep classifier and conventional machine learning methods. The proposed DeepCCI showed the best performance in all seven evaluation metrics used. In addition, the commutative property was experimentally validated. The automatically extracted features through end-to-end SMILES learning alleviates the significant efforts required for manual feature engineering. It is expected to improve prediction performance, in drug analyses.

Connected through hyperedge HE-1 (induced by source paper "mr gnn multi resolution and dual graph neural network for predicting structured entity interactions"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph edit distance computation via graph neural networks"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning graph level representation for drug discovery"
    - "representation learning on graphs methods and applications"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "relational dynamic memory networks"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "attentional multilabel learning over graphs a message passing approach"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "a deep tree based model for software defect prediction"

Connected through hyperedge HE-3 (induced by source paper "robust tumor localization with pyramid grad cam"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a survey on deep learning in medical image analysis"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

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)