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: "local similarity aware deep feature embedding"
  Abstract: Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric. To make learning more effective and efficient, hard sample mining is usually employed, with samples identified through computing the Euclidean feature distance. However, the global Euclidean distance cannot faithfully characterize the true feature similarity in a complex visual feature space, where the intraclass distance in a high-density region may be larger than the interclass distance in low-density regions. In this paper, we introduce a Position-Dependent Deep Metric (PDDM) unit, which is capable of learning a similarity metric adaptive to local feature structure. The metric can be used to select genuinely hard samples in a local neighborhood to guide the deep embedding learning in an online and robust manner. The new layer is appealing in that it is pluggable to any convolutional networks and is trained end-to-end. Our local similarity-aware feature embedding not only demonstrates faster convergence and boosted performance on two complex image retrieval datasets, its large margin nature also leads to superior generalization results under the large and open set scenarios of transfer learning and zero-shot learning on ImageNet 2010 and ImageNet-10K datasets.

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"
    - "hard aware point to set deep metric for person re identification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "decoupled networks"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-3 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-4 (induced by source paper "divide and conquer the embedding space for metric learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-5 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "zero shot sketch image hashing"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "sampling matters in deep embedding learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "boosting standard classification architectures through a ranking regularizer"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "triplet center loss for multi view 3d object retrieval"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-7 (induced by source paper "deep metric learning with alternating projections onto feasible sets"):
  with members:
    - "ranked list loss for deep metric learning"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-8 (induced by source paper "deep metric learning beyond binary supervision"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "contrastive learning for image captioning"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "siamese instance search for tracking"
    - "spice semantic propositional image caption evaluation"

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)