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: "graph transformation policy network for chemical reaction prediction"
  Abstract: We address a fundamental problem in chemistry known as chemical reaction product prediction. Our main insight is that the input reactant and reagent molecules can be jointly represented as a graph, and the process of generating product molecules from reactant molecules can be formulated as a sequence of graph transformations. To this end, we propose Graph Transformation Policy Network (GTPN) -- a novel generic method that combines the strengths of graph neural networks and reinforcement learning to learn the reactions directly from data with minimal chemical knowledge. Compared to previous methods, GTPN has some appealing properties such as: end-to-end learning, and making no assumption about the length or the order of graph transformations. In order to guide model search through the complex discrete space of sets of bond changes effectively, we extend the standard policy gradient loss by adding useful constraints. Evaluation results show that GTPN improves the top-1 accuracy over the current state-of-the-art method by about 3% on the large USPTO dataset. Our model's performances and prediction errors are also analyzed carefully in the paper.

Connected through hyperedge HE-1 (induced by source paper "augmented transformer achieves 97 and 85 for top5 prediction of direct and classical retro synthesis"):
  with members:
    - "retrosynthesis prediction with conditional graph logic network"
    - "learning to make generalizable and diverse predictions for retrosynthesis"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "graph representation learning for audio music genre classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bottom up broadcast neural network for music genre classification"
    - "fast graph representation learning with pytorch geometric"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"
    - "deepinf social influence prediction with deep learning"
    - "learning region features for object detection"

Connected through hyperedge HE-3 (induced by source paper "node injection attacks on graphs via reinforcement learning"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "adversarial policies attacking deep reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adversarial attack on graph structured data"
    - "is feature selection secure against training data poisoning"
    - "real time bidding by reinforcement learning in display advertising"
    - "semi supervised classification with graph convolutional networks"
    - "discriminative embeddings of latent variable models for structured data"

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.NE(Neural and Evolutionary Computing)