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 "hard negative mining for metric learning based zero shot classification"):
  Abstract: Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.

Includes members:
  Member "training region based object detectors with online hard example mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "zero shot visual recognition via bidirectional latent embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "improving semantic embedding consistency by metric learning for zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zero shot object detection learning to simultaneously recognize and localize novel concepts")
  Member "latent embeddings for zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "evaluation of output embeddings for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "zero shot learning via semantic similarity embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zstad zero shot temporal activity detection")
  Member "zero shot learning via joint latent similarity embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")
  Member "zero shot recognition with unreliable attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")

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)