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 "heated up softmax embedding"):
  Abstract: Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples. The problem is generally solved by learning an embedding for each sample such that the embeddings of samples of the same category are compact while the embeddings of samples of different categories are spread-out in the feature space. We study the features extracted from the second last layer of a deep neural network based classifier trained with the cross entropy loss on top of the softmax layer. We show that training classifiers with different temperature values of softmax function leads to features with different levels of compactness. Leveraging these insights, we propose a "heating-up" strategy to train a classifier with increasing temperatures, leading the corresponding embeddings to achieve state-of-the-art performance on a variety of metric learning benchmarks.

Includes members:
  Member "deep metric learning with angular loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalizing face representation with unlabeled data")
  Member "learning spread out local feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "smart mining for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "opengan open set generative adversarial networks")
  Member "learning deep embeddings with histogram loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "hard aware deeply cascaded embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a metric learning reality check")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    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)