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: "generalisation and sharing in triplet convnets for sketch based visual search"
  Abstract: We propose and evaluate several triplet CNN architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task. In contrast to recent fine-grained SBIR work, we study the ability of our networks to generalise across diverse object categories from limited training data, and explore in detail strategies for weight sharing, pre-processing, data augmentation and dimensionality reduction. We exceed the performance of pre-existing techniques on both the Flickr15k category level SBIR benchmark by $18\%$, and the TU-Berlin SBIR benchmark by $\sim10 \mathcal{T}_b$, when trained on the 250 category TU-Berlin classification dataset augmented with 25k corresponding photographs harvested from the Internet.

Connected through hyperedge HE-1 (induced by source paper "sketchformer transformer based representation for sketched structure"):
  with members:
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "image transformer"
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch r2cnn an attentive network for vector sketch recognition"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "artistic domain generalisation methods are limited by their deep representations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning to generalize meta learning for domain generalization"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "deeper broader and artier domain generalization"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "domain separation networks"
    - "domain generalization for object recognition with multi task autoencoders"
    - "detecting people in cubist art"

Connected through hyperedge HE-4 (induced by source paper "mixing realities for sketch retrieval in virtual reality"):
  with members:
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "3d shapenets a deep representation for volumetric shapes"
    - "learning fine grained image similarity with deep ranking"

Connected through hyperedge HE-5 (induced by source paper "distribution aware binarization of neural networks for sketch recognition"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "the high dimensional geometry of binary neural networks"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the power of deeper networks for expressing natural functions"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"

Connected through hyperedge HE-6 (induced by source paper "the devil is in the middle exploiting mid level representations for cross domain instance matching"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in defense of the triplet loss for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "person re identification by deep joint learning of multi loss classification"
    - "divide and fuse a re ranking approach for person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-7 (induced by source paper "deep shape matching"):
  with members:
    - "asymmetric feature maps with application to sketch based retrieval"
    - "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"
    - "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-8 (induced by source paper "generic sketch based retrieval learned without drawing a single sketch"):
  with members:
    - "asymmetric feature maps with application to sketch based retrieval"
    - "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"
    - "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)