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: "pylustrator code generation for reproducible figures for publication"
  Abstract: One major challenge in science is to make all results potentially reproducible. Thus, along with the raw data, every step from basic processing of the data, evaluation, to the generation of the figures, has to be documented as clearly as possible. While there are many programming libraries that cover the basic processing and plotting steps (e.g. Matplotlib in Python), no library yet addresses the reproducible composing of single plots into meaningful figures for publication. Thus, up to now it is still state-of-the-art to generate publishable figures using image-processing or vector-drawing software leading to unwanted alterations of the presented data in the worst case and to figure quality reduction in the best case. Pylustrator a open source library based on the Matplotlib aims to fill this gap and provides a tool to easily generate the code necessary to compose publication figures from single plots. It provides a graphical user interface where the user can interactively compose the figures. All changes are tracked and converted to code that is automatically integrated into the calling script file. Thus, this software provides the missing link from raw data to the complete plot published in scientific journals and thus contributes to the transparency of the complete evaluation procedure.

Connected through hyperedge HE-1 (induced by source paper "sparsity through evolutionary pruning prevents neuronal networks from overfitting"):
  with members:
    - "how deep is deep enough quantifying class separability in the hidden layers of deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "learning structured sparsity in deep neural networks"
    - "playing fps games with deep reinforcement learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "structured pruning of deep convolutional neural networks"
    - "on the difficulty of training recurrent neural networks"
    - "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.GR(Graphics)