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: "must cnn a multilayer shift and stitch deep convolutional architecture for sequence based protein structure prediction"
  Abstract: Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.

Connected through hyperedge HE-1 (induced by source paper "next step conditioned deep convolutional neural networks improve protein secondary structure prediction"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "chained predictions using convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "gakco a fast gapped k mer string kernel using counting"):
  with members:
    - "deep motif dashboard visualizing and understanding genomic sequences using deep neural networks"
    - "character level convolutional networks for text classification"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-3 (induced by source paper "protein secondary structure prediction using deep multi scale convolutional neural networks and next step conditioning"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepchrome deep learning for predicting gene expression from histone modifications"):
  with members:
    - "deep motif visualizing genomic sequence classifications"
    - "understanding neural networks through deep visualization"
    - "going deeper with convolutions"
    - "convolutional neural networks for sentence classification"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "recurrent convolutional neural networks for scene parsing"

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)