human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "vse improving visual semantic embeddings with hard negatives"):
  Abstract: We present a new technique for learning visual-semantic embeddings for cross-modal retrieval. Inspired by the use of hard negatives in structured prediction, and ranking loss functions used in retrieval, we introduce a simple change to common loss functions used to learn multi-modal embeddings. That, combined with fine-tuning and the use of augmented data, yields significant gains in retrieval performance. We showcase our approach, dubbed VSE++, on the MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods. On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval, and 11.3% in image retrieval (based on R@1).

Includes members:
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "learning two branch neural networks for image text matching tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "dual attention networks for multimodal reasoning and matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "instance aware image and sentence matching with selective multimodal lstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "linking image and text with 2 way nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "measuring machine intelligence through visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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.LG(Machine Learning)