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 "attract or distract exploit the margin of open set"):
  Abstract: Open set domain adaptation aims to diminish the domain shift across domains, with partially shared classes. There exist unknown target samples out of the knowledge of source domain. Compared to the close set setting, how to separate the unknown (unshared) class from the known (shared) ones plays a key role. Whereas, previous methods did not emphasize the semantic structure of the open set data, which may introduce bias into the domain alignment and confuse the classifier around the decision boundary. In this paper, we exploit the semantic structure of open set data from two aspects: 1) Semantic Categorical Alignment, which aims to achieve good separability of target known classes by categorically aligning the centroid of target with the source. 2)Semantic Contrastive Mapping, which aims to push the unknown class away from the decision boundary. Empirically, we demonstrate that our method performs favourably against the state-of-the-art methods on representative benchmarks, e.g. Digit datasets and Office-31 datasets.

Includes members:
  Member "contrastive adaptation network for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "invariance matters exemplar memory for domain adaptive person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "significance aware information bottleneck for domain adaptive semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "label driven reconstruction for domain adaptation in semantic segmentation")
  Member "open set domain adaptation by backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "learning factorized representations for open set domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "known class aware self ensemble for open set domain adaptation")
  Member "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "virtual class enhanced discriminative embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mixed high order attention network for person re identification")

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)