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 "clarel classification via retrieval loss for zero shot learning"):
  Abstract: We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. On top of that, we provide a probabilistic justification for a metric rescaling approach that solves a very common problem in the generalized zero-shot learning setting, i.e., classifying test images from unseen classes as one of the classes seen during training. We evaluate our approach on two fine-grained zero-shot learning datasets: CUB and FLOWERS. We find that on the generalized zero-shot classification task CLAREL consistently outperforms the existing approaches on both datasets.

Includes members:
  Member "f vaegan d2 a feature generating framework for any shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "zero shot image recognition using relational matching adaptation and calibration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bi semantic reconstructing generative network for zero shot learning")
  Member "generalized zero and few shot learning via aligned variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "multi modal cycle consistent generalized 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 "probabilistic and or attribute grouping for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain aware generalized zero shot learning")
  Member "adaptive confidence smoothing for generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locality and compositionality in zero shot learning")
  Member "learning robust visual semantic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn")
  Member "stackgan realistic image synthesis with stacked generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial networks")

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.CV(Computer Vision and Pattern Recognition)