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 "from classical to generalized zero shot learning a simple adaptation process"):
  Abstract: Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training examples of seen classes. While classical ZSL considers the recognition performance on unseen classes only, generalized zero-shot learning (GZSL) aims at maximizing performance on both seen and unseen classes. In this paper, we propose a new process for training and evaluation in the GZSL setting; this process addresses the gap in performance between samples from unseen and seen classes by penalizing the latter, and enables to select hyper-parameters well-suited to the GZSL task. It can be applied to any existing ZSL approach and leads to a significant performance boost: the experimental evaluation shows that GZSL performance, averaged over eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from 28.2 to 57.1 on AwA2.

Includes members:
  Member "semantic autoencoder for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crl class representative learning for image classification")
  Member "generating visual representations for zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal cycle consistent generalized zero shot learning")
  Member "zero shot learning a comprehensive evaluation of the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "feature generating networks for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "label embedding for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crl class representative learning for image classification")
  Member "synthesized classifiers for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "an empirical study and analysis of generalized zero shot learning for object recognition in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition")

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)