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 the architecture of deep neural networks"
  Abstract: Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We introduce a new trainable parameter called tri-state ReLU, which helps in eliminating unnecessary neurons. We also propose a smooth regularizer which encourages the total number of neurons after elimination to be small. The resulting objective is differentiable and simple to optimize. We experimentally validate our method on both small and large networks, and show that it can learn models with a considerably small number of parameters without affecting prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "the lottery ticket hypothesis finding sparse trainable neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking the value of network pruning"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep rewiring training very sparse deep networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "nisp pruning networks using neuron importance score propagation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "viton an image based virtual try on network"
    - "c wsl count guided weakly supervised localization"
    - "blockdrop dynamic inference paths in residual networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "deep regionlets for object detection"

Connected through hyperedge HE-3 (induced by source paper "online deep learning learning deep neural networks on the fly"):
  with members:
    - "lifelong learning with dynamically expandable networks"
    - "sol a library for scalable online learning algorithms"
    - "deep networks with stochastic depth"
    - "fractalnet ultra deep neural networks without residuals"
    - "neural architecture search with reinforcement learning"
    - "learning the number of neurons in deep networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "training sparse neural networks"):
  with members:
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "data free parameter pruning for deep neural networks"
    - "acdc a structured efficient linear layer"
    - "learning neural network architectures using backpropagation"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "fitnets hints for thin deep nets"

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)