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: "understanding and improving convolutional neural networks via concatenated rectified linear units"
  Abstract: Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision. In this paper, we aim to provide insight on the property of convolutional neural networks, as well as a generic method to improve the performance of many CNN architectures. Specifically, we first examine existing CNN models and observe an intriguing property that the filters in the lower layers form pairs (i.e., filters with opposite phase). Inspired by our observation, we propose a novel, simple yet effective activation scheme called concatenated ReLU (CRelu) and theoretically analyze its reconstruction property in CNNs. We integrate CRelu into several state-of-the-art CNN architectures and demonstrate improvement in their recognition performance on CIFAR-10/100 and ImageNet datasets with fewer trainable parameters. Our results suggest that better understanding of the properties of CNNs can lead to significant performance improvement with a simple modification.

Connected through hyperedge HE-1 (induced by source paper "exploiting the redundancy in convolutional filters for parameter reduction"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "learning filter basis for convolutional neural network compression"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "soft root sign activation function"):
  with members:
    - "mish a self regularized non monotonic neural activation function"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improving deep learning by inverse square root linear units isrlus"
    - "invertible residual networks"
    - "the quest for the golden activation function"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "neural network compression framework for fast model inference"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "learning sparse networks using targeted dropout"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "detection of the prodromal phase of bipolar disorder from psychological and phonological aspects in social media"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "identifying depression on twitter"

Connected through hyperedge HE-5 (induced by source paper "tadam a robust stochastic gradient optimizer"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximal policy optimization algorithms"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "neural epitome search for architecture agnostic network compression"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "low bit quantization of neural networks for efficient inference"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-7 (induced by source paper "large scale adversarial representation learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "data efficient image recognition with contrastive predictive coding"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-8 (induced by source paper "a survey of pruning methods for efficient person re identification across domains"):
  with members:
    - "play and prune adaptive filter pruning for deep model compression"
    - "building efficient convnets using redundant feature pruning"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "an entropy based pruning method for cnn compression"
    - "channel pruning for accelerating very deep neural networks"

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)