human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "recover and identify a generative dual model for cross resolution person re identification"
  Abstract: Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person 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 person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training.

Connected through hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "learning resolution invariant deep representations for person re identification"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning blind video temporal consistency"

Connected through hyperedge HE-3 (induced by source paper "learning shape representations for clothing variations in person re identification"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "joint discriminative and generative learning for person re identification"
    - "rgb infrared cross modality person re identification via joint pixel and feature alignment"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "deep representation learning with part loss for person re identification"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"

Connected through hyperedge HE-4 (induced by source paper "cross dataset person re identification via unsupervised pose disentanglement and adaptation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "cgans with projection discriminator"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "learning resolution invariant deep representations for person re identification"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "improving person re identification by attribute and identity learning"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "deep learning for malicious flow detection"
    - "multi level factorisation net for person re identification"
    - "multimodal unsupervised image to image translation"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "deep group shuffling random walk for person re identification"

Question: Please tell me which class the center node 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)