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: "dynamic capacity networks"
  Abstract: We introduce the Dynamic Capacity Network (DCN), a neural network that can adaptively assign its capacity across different portions of the input data. This is achieved by combining modules of two types: low-capacity sub-networks and high-capacity sub-networks. The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks. The selection is made using a novel gradient-based attention mechanism, that efficiently identifies input regions for which the DCN's output is most sensitive and to which we should devote more capacity. We focus our empirical evaluation on the Cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are able to drastically reduce the number of computations, compared to traditional convolutional neural networks, while maintaining similar or even better performance.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "dynamic convolutions exploiting spatial sparsity for faster inference"):
  with members:
    - "batch shaping for learning conditional channel gated networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sbnet sparse blocks network for fast inference"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-3 (induced by source paper "attend to count crowd counting with adaptive capacity multi scale cnns"):
  with members:
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "crowd counting using scale aware attention networks"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "context aware crowd counting"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-4 (induced by source paper "learning instance wise sparsity for accelerating deep models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "channel gating neural networks"
    - "sbnet sparse blocks network for fast inference"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "adaptive neural networks for efficient inference"

Connected through hyperedge HE-5 (induced by source paper "needles in haystacks on classifying tiny objects in large images"):
  with members:
    - "pathologist level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "r 2 cnn fast tiny object detection in large scale remote sensing images"
    - "processing megapixel images with deep attention sampling models"
    - "attention based deep multiple instance learning"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "exploring the limits of weakly supervised pretraining"
    - "3d multirater rcnn for multimodal multiclass detection and characterisation of extremely small objects"

Connected through hyperedge HE-6 (induced by source paper "stampnet unsupervised multi class object discovery"):
  with members:
    - "yolov3 an incremental improvement"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "recurrent spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "attentive recurrent comparators"

Connected through hyperedge HE-8 (induced by source paper "maskconnect connectivity learning by gradient descent"):
  with members:
    - "multi fiber networks for video recognition"
    - "efficient neural architecture search via parameter sharing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "large scale evolution of image classifiers"
    - "blockdrop dynamic inference paths in residual networks"
    - "hierarchical representations for efficient architecture search"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "pathnet evolution channels gradient descent in super 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)