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: "deep shape matching"
  Abstract: We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.

Connected through hyperedge HE-1 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "identifying cross depicted historical motifs"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-3 (induced by source paper "high level perceptual similarity is enabled by learning diverse tasks"):
  with members:
    - "totally looks like how humans compare compared to machines"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "learning to attend in a brain inspired deep neural network"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "livesketch query perturbations for guided sketch based visual search"):
  with members:
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "asymmetric feature maps with application to sketch based retrieval"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "synthesizing robust adversarial examples"
    - "a neural representation of sketch drawings"
    - "deep image retrieval learning global representations for image search"
    - "generalisation and sharing in triplet convnets for sketch based visual search"

Connected through hyperedge HE-5 (induced by source paper "no fear of the dark image retrieval under varying illumination conditions"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "learning to see in the dark"
    - "billion scale similarity search with gpus"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "deep image retrieval learning global representations for image search"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "linking art through human poses"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "weakly supervised object detection in artworks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "rmpe regional multi person pose estimation"
    - "detecting people in artwork with cnns"
    - "you only look once unified real time object detection"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-7 (induced by source paper "instance level sketch based retrieval by deep triplet classification siamese network"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "densely connected convolutional networks"
    - "deepid3 face recognition with very deep neural networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "show attend and tell neural image caption generation with visual attention"

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)