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 "cross resolution adversarial dual network for person re identification and beyond"):
  Abstract: Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Extensive experimental results on five standard person re-ID benchmarks confirm the effectiveness of our method and the superiority over the state-of-the-art approaches, especially when the input resolutions are not seen during training. Furthermore, the experimental results on two vehicle re-ID benchmarks also confirm the generalization of our model on cross-resolution visual tasks. The extensions of semi-supervised settings further support the use of our proposed approach to real-world scenarios and applications.

Includes members:
  Member "interaction and aggregation network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "recover and identify a generative dual model for cross resolution person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "cross dataset person re identification via unsupervised pose disentanglement and adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "improving person re identification by attribute and identity learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object centric image generation from layouts")
  Member "multi level factorisation net for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "learning to adapt structured output space for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)