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: "hard aware point to set deep metric for person re identification"
  Abstract: Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion. Deep metric learning provides a satisfactory solution to person re-ID by training a deep network under supervision of metric loss, e.g., triplet loss. However, the performance of deep metric learning is greatly limited by traditional sampling methods. To solve this problem, we propose a Hard-Aware Point-to-Set (HAP2S) loss with a soft hard-mining scheme. Based on the point-to-set triplet loss framework, the HAP2S loss adaptively assigns greater weights to harder samples. Several advantageous properties are observed when compared with other state-of-the-art loss functions: 1) Accuracy: HAP2S loss consistently achieves higher re-ID accuracies than other alternatives on three large-scale benchmark datasets; 2) Robustness: HAP2S loss is more robust to outliers than other losses; 3) Flexibility: HAP2S loss does not rely on a specific weight function, i.e., different instantiations of HAP2S loss are equally effective. 4) Generality: In addition to person re-ID, we apply the proposed method to generic deep metric learning benchmarks including CUB-200-2011 and Cars196, and also achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "informative sample mining network for multi domain image to image translation"):
  with members:
    - "hardness aware deep metric learning"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "relgan multi domain image to image translation via relative attributes"
    - "sampling matters in deep embedding learning"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep metric learning with angular loss"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (induced by source paper "symmetrical synthesis for deep metric learning"):
  with members:
    - "hardness aware deep metric learning"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "deep metric learning with angular loss"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-3 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "feature mask network for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"

Connected through hyperedge HE-4 (induced by source paper "backbone can not be trained at once rolling back to pre trained network for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "do better imagenet models transfer better"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deep mutual learning"

Connected through hyperedge HE-5 (induced by source paper "hardness aware deep metric learning"):
  with members:
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-6 (induced by source paper "a simple and effective framework for pairwise deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "revisiting metric learning for few shot image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "large scale distance metric learning with uncertainty"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-7 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval"):
  with members:
    - "combination of multiple global descriptors for image retrieval"
    - "learning type aware embeddings for fashion compatibility"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "began boundary equilibrium generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "squeeze and excitation networks"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "temporal knowledge propagation for image to video person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "vrstc occlusion free video person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"
    - "deep mutual learning"
    - "alignedreid surpassing human level performance in person re identification"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

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)