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: "input switched affine networks an rnn architecture designed for interpretability"
  Abstract: There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations - in other words an RNN without any explicit nonlinearities, but with input-dependent recurrent weights. This simple form allows the RNN to be analyzed via straightforward linear methods: we can exactly characterize the linear contribution of each input to the model predictions; we can use a change-of-basis to disentangle input, output, and computational hidden unit subspaces; we can fully reverse-engineer the architecture's solution to a simple task. Despite this ease of interpretation, the input switched affine network achieves reasonable performance on a text modeling tasks, and allows greater computational efficiency than networks with standard nonlinearities.

Connected through hyperedge HE-1 (induced by source paper "tensor networks for language modeling"):
  with members:
    - "singular value automata and approximate minimization"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "attention is all you need"
    - "controlling linguistic style aspects in neural language generation"
    - "language as a matrix product state"
    - "regularizing and optimizing lstm language models"
    - "tensor network language model"
    - "language design as information renormalization"

Connected through hyperedge HE-2 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-4 (induced by source paper "relative attributing propagation interpreting the comparative contributions of individual units in deep neural networks"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "pixel objectness learning to segment generic objects automatically in images and videos"
    - "interpreting deep visual representations via network dissection"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-5 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards proving the adversarial robustness of deep neural networks"
    - "output range analysis for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "dimension reduction approach for interpretability of sequence to sequence recurrent neural networks"):
  with members:
    - "on human motion prediction using recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "visualizing and understanding recurrent networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "language modeling for code switching evaluation integration of monolingual data and discriminative training"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "large margin neural language model"
    - "on the state of the art of evaluation in neural language models"
    - "intelligible language modeling with input switched affine networks"
    - "regularizing and optimizing lstm language models"
    - "language modeling for code switched data challenges and approaches"
    - "dual language models for code mixed speech recognition"

Connected through hyperedge HE-8 (induced by source paper "gated orthogonal recurrent units on learning to forget"):
  with members:
    - "associative long short term memory"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "gated graph sequence neural networks"
    - "recurrent orthogonal networks and long memory tasks"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "recurrent batch normalization"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "listen attend and spell"

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.AI(Artificial Intelligence)