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: "fundamentals of recurrent neural network rnn and long short term memory lstm network"
  Abstract: Abstract   Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training formulas are omitted altogether. In addition, the technique of “unrolling” an RNN is routinely presented without justification throughout the literature. The goal of this tutorial is to explain the essential RNN and LSTM fundamentals in a single document. Drawing from concepts in Signal Processing, we formally derive the canonical RNN formulation from differential equations. We then propose and prove a precise statement, which yields the RNN unrolling technique. We also review the difficulties with training the standard RNN and address them by transforming the RNN into the “Vanilla LSTM” 1   network through a series of logical arguments. We provide all equations pertaining to the LSTM system together with detailed descriptions of its constituent entities. Albeit unconventional, our choice of notation and the method for presenting the LSTM system emphasizes ease of understanding. As part of the analysis, we identify new opportunities to enrich the LSTM system and incorporate these extensions into the Vanilla LSTM network, producing the most general LSTM variant to date. The target reader has already been exposed to RNNs and LSTM networks through numerous available resources and is open to an alternative pedagogical approach. A Machine Learning practitioner seeking guidance for implementing our new augmented LSTM model in software for experimentation and research will find the insights and derivations in this treatise valuable as well.

Connected through hyperedge HE-1 (induced by source paper "a cnn rnn framework for crop yield prediction"):
  with members:
    - "classification of crop tolerance to heat and drought a deep convolutional neural networks approach"
    - "crop yield prediction using deep neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-2 (induced by source paper "classification of traffic using neural networks by rejecting a novel approach in classifying vpn traffic"):
  with members:
    - "deep rbf networks revisited robust classification with rejection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "automated search for configurations of deep neural network architectures"):
  with members:
    - "finding faster configurations using flash"
    - "efficient neural architecture search via parameter sharing"
    - "searching for test data with feature diversity"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "lgn cnn a biologically inspired cnn architecture"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "learning rotation invariant convolutional filters for texture classification"
    - "flip rotate pooling convolution and split dropout on convolution neural networks for image classification"
    - "matconvnet convolutional neural networks for matlab"

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)