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: "locally scale invariant convolutional neural networks"
  Abstract: Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks. With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales. Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain changes in appearance. Recent results on the ImageNet dataset show that given enough data, ConvNets can learn such invariances producing very discriminative features [1]. But could we do more: use less parameters, less data, learn more discriminative features, if certain invariances were built into the learning process? In this paper we present a simple model that allows ConvNets to learn features in a locally scale-invariant manner without increasing the number of model parameters. We show on a modified MNIST dataset that when faced with scale variation, building in scale-invariance allows ConvNets to learn more discriminative features with reduced chances of over-fitting.

Connected through hyperedge HE-1 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "interaction and aggregation network for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "cbam convolutional block attention module"
    - "resource aware person re identification across multiple resolutions"
    - "non local neural networks"
    - "scalable person re identification on supervised smoothed manifold"
    - "active convolution learning the shape of convolution for image classification"

Connected through hyperedge HE-3 (induced by source paper "towards learning affine invariant representations via data efficient cnns"):
  with members:
    - "gabor convolutional networks"
    - "differentiable mpc for end to end planning and control"
    - "dynamic filtering with large sampling field for convnets"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-4 (induced by source paper "deformable kernels adapting effective receptive fields for object deformation"):
  with members:
    - "deformable filter convolution for point cloud reasoning"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "making convolutional networks shift invariant again"
    - "learning by abstraction the neural state machine"
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "dynamic scale inference by entropy minimization"
    - "kpconv flexible and deformable convolution for point clouds"
    - "selective kernel networks"

Connected through hyperedge HE-5 (induced by source paper "blurring the line between structure and learning to optimize and adapt receptive fields"):
  with members:
    - "making convolutional networks shift invariant again"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "deep layer aggregation"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "dynamic scale inference by entropy minimization"):
  with members:
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "semi supervised domain adaptation via minimax entropy"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "understanding the impact of entropy on policy optimization"
    - "on calibration of modern neural networks"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "deep layer aggregation"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "self supervised scale equivariant network for weakly supervised semantic segmentation"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "unsupervised representation learning by predicting image rotations"
    - "self erasing network for integral object attention"
    - "learning random walk label propagation for weakly supervised semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

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.CV(Computer Vision and Pattern Recognition)