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: "multi scale spatially asymmetric recalibration for image classification"
  Abstract: Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition. This paper addresses this issue by introducing a recalibration process, which refers to the surrounding region of each neuron, computes an importance value and multiplies it to the original neural response. Our approach is named multi-scale spatially-asymmetric recalibration (MS-SAR), which extracts visual cues from surrounding regions at multiple scales, and designs a weighting scheme which is asymmetric in the spatial domain. MS-SAR is implemented in an efficient way, so that only small fractions of extra parameters and computations are required. We apply MS-SAR to several popular building blocks, including the residual block and the densely-connected block, and demonstrate its superior performance in both CIFAR and ILSVRC2012 classification tasks.

Connected through hyperedge HE-1 (induced by source paper "rethinking normalization and elimination singularity in neural networks"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-2 (induced by source paper "multi scale dual branch fully convolutional network for hand parsing"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "progressive refinement a method of coarse to fine image parsing using stacked network"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning to estimate 3d hand pose from single rgb images"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "neural rejuvenation improving deep network training by enhancing computational resource utilization"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "deep clustering for unsupervised learning of visual features"
    - "stacked cross attention for image text matching"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep co training for semi supervised image recognition"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep co training for semi supervised image recognition"):
  with members:
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "few shot image recognition by predicting parameters from activations"
    - "sort second order response transform for visual recognition"
    - "gradually updated neural networks for large scale image recognition"
    - "introspective classification with convolutional nets"
    - "single shot object detection with enriched semantics"
    - "adversarial examples for semantic segmentation and object detection"

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)