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 "open cross domain visual search"):
  Abstract: This paper introduces open cross-domain visual search, where categories in any target domain are retrieved based on queries from any source domain. Current works usually tackle cross-domain visual search as a domain adaptation problem. This limits the search to a closed setting, with one fixed source domain and one fixed target domain. To make the step towards an open setting where multiple visual domains are available, we introduce a simple yet effective approach. We formulate the search as one of mapping examples from every visual domain to a common semantic space, where categories are represented by hyperspherical prototypes. Cross-domain search is then performed by searching in the common space, regardless of which domains are used as source or target. Having separate mappings for every domain allows us to search in an open setting, and to incrementally add new domains over time without retraining existing mapping functions. Experimentally, we show our capability to perform open cross-domain visual search. Our approach is competitive with respect to traditional closed settings, where we obtain state-of-the-art results on six benchmarks for three sketch-based search tasks.

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 "hyperspherical prototype networks":
    also connected through other hyperedges:
      (none)
  Member "unsupervised open domain recognition by semantic discrepancy minimization":
    also connected through other hyperedges:
      (none)
  Member "semantic aware knowledge preservation for zero shot sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain adaptive ensemble learning")
  Member "doodle to search practical zero shot sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "domain generalization via model agnostic learning of semantic features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive ensemble learning")
  Member "moment matching for multi source domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")

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)