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: "low rank passthrough neural networks"
  Abstract: Deep learning consists in training neural networks to perform computations that sequentially unfold in many steps over a time dimension or an intrinsic depth dimension. Effective learning in this setting is usually accomplished by specialized network architectures that are designed to mitigate the vanishing gradient problem of naive deep networks. Many of these architectures, such as LSTMs, GRUs, Highway Networks and Deep Residual Network, are based on a single structural principle: the state passthrough. #R##N#We observe that these architectures, hereby characterized as Passthrough Networks, in addition to the mitigation of the vanishing gradient problem, enable the decoupling of the network state size from the number of parameters of the network, a possibility that is exploited in some recent works but not thoroughly explored. #R##N#In this work we propose simple, yet effective, low-rank and low-rank plus diagonal matrix parametrizations for Passthrough Networks which exploit this decoupling property, reducing the data complexity and memory requirements of the network while preserving its memory capacity. We present competitive experimental results on synthetic tasks and a near state of the art result on sequential randomly-permuted MNIST classification, a hard task on natural data.

Connected through hyperedge HE-1 (induced by source paper "on the effectiveness of low rank matrix factorization for lstm model compression"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "deep contextualized word representations"
    - "end to end neural coreference resolution"
    - "on the state of the art of evaluation in neural language models"
    - "learned in translation contextualized word vectors"
    - "learning intrinsic sparse structures within long short term memory"
    - "tensor train recurrent neural networks for video classification"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (induced by source paper "low rank approximation from communication complexity"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "relative error tensor low rank approximation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "greedy bi criteria approximations for k medians and k means"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"

Connected through hyperedge HE-3 (induced by source paper "human motion prediction via spatio temporal inpainting"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "convolutional sequence to sequence model for human dynamics"
    - "a style based generator architecture for generative adversarial networks"
    - "human motion analysis with deep metric learning"
    - "a neural network approach to missing marker reconstruction in human motion capture"
    - "which training methods for gans do actually converge"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "constrained deep learning using conditional gradient and applications in computer vision"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "proximal backpropagation"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "imposing hard constraints on deep networks promises and limitations"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "an overview of multi task learning in deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "kronecker recurrent units"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "orthogonal rnns and long memory tasks"
    - "recurrent highway networks"
    - "gradient descent learns linear dynamical systems"
    - "a simple way to initialize recurrent networks of rectified linear units"

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)