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 "an optimal transport framework for zero shot learning"):
  Abstract: We present an optimal transport (OT) framework for generalized zero-shot learning (GZSL) of imaging data, seeking to distinguish samples for both seen and unseen classes, with the help of auxiliary attributes. The discrepancy between features and attributes is minimized by solving an optimal transport problem. {Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.} The generative model and the optimal transport are optimized iteratively with an attribute-based regularizer, that further enhances the discriminative power of the generated features. A classifier is learned based on the features generated for both the seen and unseen classes. In addition to generalized zero-shot learning, our framework is also applicable to standard and transductive ZSL problems. Experiments show that our optimal transport-based method outperforms state-of-the-art methods on several benchmark datasets.

Includes members:
  Member "improving textual network embedding with global attention via optimal transport":
    also connected through other hyperedges:
      (none)
  Member "topic guided variational autoencoders for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words")
  Member "improving textual network learning with variational homophilic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph driven generative models for heterogeneous multi task learning")
  Member "zero shot recognition via semantic embeddings and knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "discriminative learning of latent features for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning for new visual environments with limited labels")
  Member "nash toward end to end neural architecture for generative semantic hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "distilled wasserstein learning for word embedding and topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "zero shot kernel learning":
    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")

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)