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: "efficient elastic net regularization for sparse linear models"
  Abstract: We extend previous work on efficiently training linear model s by applying stochastic updates to non-zero features only, lazily bring ing weights current as needed. To date, only the closed form updates for the l1, l1, and the rarely used l2 norm have been described. We extend this work by showing the proper closed form updates for the popular l 2 and elastic net regularized models. We show a dynamic programming algorithm to calculate the proper elastic net update with only one constant-time subproblem computation per update. Our algorithm handles both fixed and decreasing learning rates and we derive th e result for both stochastic gradient descent (SGD) and forward backward splitting (FoBoS) . We empirically validate the algorithm, showing that on a bag-of-words dataset with 260, 941 features and 88 nonzero features on average per example, our method trains a logistic regression classifier with elastic net reg ularization 612 times faster than an otherwise identical implementation with dense updates.

Connected through hyperedge HE-1 (induced by source paper "a critical review of recurrent neural networks for sequence learning"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "sequence to sequence video to text"
    - "neural turing machines"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning to execute"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

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)