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 randomized ensembles for metric learning"
  Abstract: Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In this work, we propose a novel, generalizable and fast method to define a family of embedding functions that can be used as an ensemble to give improved results. Each embedding function is learned by randomly bagging the training labels into small subsets. We show experimentally that these embedding ensembles create effective embedding functions. The ensemble output defines a metric space that improves state of the art performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes Retrieval and VehicleID.

Connected through hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning"):
  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"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "softtriple loss deep metric learning without triplet sampling"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-2 (induced by source paper "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "classification is a strong baseline for deep metric learning"
    - "learning to navigate for fine grained classification"
    - "spottune transfer learning through adaptive fine tuning"

Connected through hyperedge HE-3 (induced by source paper "combination of multiple global descriptors for image retrieval"):
  with members:
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019"
    - "visualizing deep similarity networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "learning discriminative features with multiple granularities for person re identification"
    - "classification is a strong baseline for deep metric learning"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (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"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-6 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"
    - "videos as space time region graphs"

Connected through hyperedge HE-7 (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"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep unsupervised learning of visual similarities"

Connected through hyperedge HE-8 (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 metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"

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)