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: "asymmetric feature maps with application to sketch based retrieval"
  Abstract: We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.

Connected through hyperedge HE-1 (induced by source paper "deep semantic parsing of freehand sketches with homogeneous transformation soft weighted loss and staged learning"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "sketchyscene richly annotated scene sketches"
    - "random erasing data augmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "semi heterogeneous three way joint embedding network for sketch based image retrieval"):
  with members:
    - "person re identification by semantic region representation and topology constraint"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning deep sketch abstraction"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "harmonious attention network for person re identification"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (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"
    - "deep shape matching"
    - "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-4 (induced by source paper "deep shape matching"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "deeper broader and artier domain generalization"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "generalisation and sharing in triplet convnets for sketch based visual search"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-5 (induced by source paper "generic sketch based retrieval learned without drawing a single sketch"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "generalisation and sharing in triplet convnets for sketch based visual search"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

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)