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: "no fuss distance metric learning using proxies"
  Abstract: We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.

Connected through hyperedge HE-1 (induced by source paper "opengan open set generative adversarial networks"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "the importance of metric learning for robotic vision open set recognition and active learning"
    - "cgans with projection discriminator"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "spectral normalization for generative adversarial networks"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a metric learning reality check"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "unbiased evaluation of deep metric learning algorithms"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "momentum contrast for unsupervised visual representation learning"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "signal to noise ratio a robust distance metric for deep metric learning"

Connected through hyperedge HE-4 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking and vehicle re id in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"

Connected through hyperedge HE-5 (induced by source paper "learning transformation aware embeddings for image forensics"):
  with members:
    - "learning rich features for image manipulation detection"
    - "classification is a strong baseline for deep metric learning"
    - "fighting fake news image splice detection via learned self consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "image processing operations identification via convolutional neural network"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"

Connected through hyperedge HE-6 (induced by source paper "unifying specialist image embedding into universal image embedding"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "relational knowledge distillation"
    - "unifying heterogeneous classifiers with distillation"
    - "learning deep representations with probabilistic knowledge transfer"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "an analysis of object embeddings for image retrieval"):
  with members:
    - "learning rich features for image manipulation detection"
    - "do better imagenet models transfer better"
    - "deep learning for generic object detection a survey"
    - "dgc net dense geometric correspondence network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "the ps battles dataset an image collection for image manipulation detection"
    - "sniper efficient multi scale training"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-8 (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"

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)