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: "improved embeddings with easy positive triplet mining"
  Abstract: Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant locations. Substantial work has focused on loss functions and strategies to learn these embeddings by pushing images from the same class as close together in the embedding space as possible. In this paper, we propose an alternative, loosened embedding strategy that requires the embedding function only map each training image to the most similar examples from the same class, an approach we call "Easy Positive" mining. We provide a collection of experiments and visualizations that highlight that this Easy Positive mining leads to embeddings that are more flexible and generalize better to new unseen data. This simple mining strategy yields recall performance that exceeds state of the art approaches (including those with complicated loss functions and ensemble methods) on image retrieval datasets including CUB, Stanford Online Products, In-Shop Clothes and Hotels-50K.

Connected through hyperedge HE-1 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hardness aware deep metric learning"
    - "visualizing how embeddings generalize"
    - "divide and conquer the embedding space for metric learning"
    - "visual explanation for deep metric learning"
    - "visualizing deep similarity networks"
    - "deep metric learning beyond binary supervision"
    - "grounding visual explanations"

Connected through hyperedge HE-2 (induced by source paper "embedding expansion augmentation in embedding space for deep metric learning"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "mixup beyond empirical risk minimization"
    - "between class learning for image classification"
    - "deep variational information bottleneck"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "trusted confidence bounds for learning enabled cyber physical systems"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "on calibration of modern neural networks"
    - "deep metric learning using triplet network"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-4 (induced by source paper "traffickcam explainable image matching for sex trafficking investigations"):
  with members:
    - "hotels 50k a global hotel recognition dataset"
    - "visualizing deep similarity networks"
    - "visual interpretability for deep learning a survey"
    - "semi supervised learning for detecting human trafficking"
    - "combating human trafficking with deep multimodal models"
    - "faceless person recognition privacy implications in social media"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "visualizing how embeddings generalize"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "imagenet large scale visual recognition challenge"

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)