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 "a zero shot framework for sketch based image retrieval"):
  Abstract: Sketch-based image retrieval (SBIR) is the task of retrieving images from a natural image database that correspond to a given hand-drawn sketch. Ideally, an SBIR model should learn to associate components in the sketch (say, feet, tail, etc.) with the corresponding components in the image having similar shape characteristics. However, current evaluation methods simply focus only on coarse-grained evaluation where the focus is on retrieving images which belong to the same class as the sketch but not necessarily having the same shape characteristics as in the sketch. As a result, existing methods simply learn to associate sketches with classes seen during training and hence fail to generalize to unseen classes. In this paper, we propose a new benchmark for zero-shot SBIR where the model is evaluated in novel classes that are not seen during training. We show through extensive experiments that existing models for SBIR that are trained in a discriminative setting learn only class specific mappings and fail to generalize to the proposed zero-shot setting. To circumvent this, we propose a generative approach for the SBIR task by proposing deep conditional generative models that take the sketch as an input and fill the missing information stochastically. Experiments on this new benchmark created from the "Sketchy" dataset, which is a large-scale database of sketch-photo pairs demonstrate that the performance of these generative models is significantly better than several state-of-the-art approaches in the proposed zero-shot framework of the coarse-grained SBIR task.

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 "an empirical evaluation of visual question answering for novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization")
  Member "a simple exponential family framework for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "style transfer generative adversarial networks learning to play chess differently":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets")
  Member "a generative model for zero shot learning using conditional variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning")
  Member "deep sketch hashing fast free hand sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a network pruning network approach to deep model compression")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crl class representative learning for image classification")
  Member "fast zero shot image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "captioning images with novel objects via online vocabulary expansion")

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)