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: "learning implicitly recurrent cnns through parameter sharing"
  Abstract: We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy. #R##N#Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops. Training these networks thus implicitly involves discovery of suitable recurrent architectures. Though considering only the design aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures. #R##N#Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias. Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.

Connected through hyperedge HE-1 (induced by source paper "full stack filters to build minimum viable cnns"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "constructing fast network through deconstruction of convolution"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "rethinking the value of network pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "discovering neural wirings"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "sparse networks from scratch faster training without losing performance"
    - "learning sparse networks using targeted dropout"
    - "luck matters understanding training dynamics of deep relu networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "winning the lottery with continuous sparsification"):
  with members:
    - "soft threshold weight reparameterization for learnable sparsity"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "sparse transfer learning via winning lottery tickets"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "training sparse neural networks"

Connected through hyperedge HE-4 (induced by source paper "relaxed weight sharing effectively modeling time varying relationships in clinical time series"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scalable and accurate deep learning for electronic health records"
    - "attention is all you need"
    - "early improving recurrent elastic highway network"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "unitary evolution recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "deep reinforcement learning with double q learning"

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)