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 "bi directional domain translation for zero shot sketch based image retrieval"):
  Abstract: The goal of Sketch-Based Image Retrieval (SBIR) is using free-hand sketches to retrieve images of the same category from a natural image gallery. However, SBIR requires all categories to be seen during training, which cannot be guaranteed in real-world applications. So we investigate more challenging Zero-Shot SBIR (ZS-SBIR), in which test categories do not appear in the training stage. Traditional SBIR methods are prone to be category-based retrieval and cannot generalize well from seen categories to unseen ones. In contrast, we disentangle image features into structure features and appearance features to facilitate structure-based retrieval. To assist feature disentanglement and take full advantage of disentangled information, we propose a Bi-directional Domain Translation (BDT) framework for ZS-SBIR, in which the image domain and sketch domain can be translated to each other through disentangled structure and appearance features. Finally, we perform retrieval in both structure feature space and image feature space. Extensive experiments demonstrate that our proposed approach remarkably outperforms state-of-the-art approaches by about 8% on the Sketchy dataset and over 5% on the TU-Berlin dataset.

Includes members:
  Member "semantically tied paired cycle consistency for zero shot sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "semantic aware knowledge preservation for zero shot sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "semantic adversarial network for zero shot sketch based image retrieval":
    also connected through other hyperedges:
      (none)
  Member "stacked semantic guided network for zero shot sketch based image retrieval":
    also connected through other hyperedges:
      (none)
  Member "a unified feature disentangler for multi domain image translation and manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantically tied paired cycle consistency for zero shot sketch based image retrieval")
  Member "zero shot sketch image hashing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")

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)