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: "feature mask network for person re identification"
  Abstract: Abstract   Person re-identification aims at establishing the identity of a pedestrian from a gallery that contains images of people obtained from a multi-camera system, which has many applications in video surveillance for public security and safety. Many challenges such as occlusions, drastic lighting and pose variations across the camera views, and noise make this task highly challenging. While most approaches focus on learning features and metrics to derive better representations, we hypothesize that both local and global contextual cues are crucial for an accurate identity matching. To this end, we propose a Feature Mask Network (FMN) that takes advantage of ResNet high-level features to predict a feature map mask and then imposes it on the low-level features to dynamically re-weight different object parts for a complementary feature representation. This serves as an attention mechanism by allowing the network to focus on local details selectively. We frame the network training as a multi-task objective optimization, which further improves the learned feature descriptions. We conduct experiments on Market-1501, DukeMTMC-reID and CUHK03 datasets, where the proposed approach respectively achieves significant improvements and competitive results when compared to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "progressive sample mining and representation learning for one shot person re identification with adversarial samples"):
  with members:
    - "unsupervised tracklet person re identification"
    - "weakly supervised person re identification"
    - "improving person re identification by attribute and identity learning"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "self similarity grouping a simple unsupervised cross domain adaptation approach for person re identification"
    - "attention aware compositional network for person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-3 (induced by source paper "aanet attribute attention network for person re identifications"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "semantic correlation promoted shape variant context for segmentation"
    - "improving person re identification by attribute and identity learning"
    - "end to end deep kronecker product matching for person re identification"
    - "re id done right towards good practices for person re identification"
    - "human semantic parsing for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "hornet a hierarchical offshoot recurrent network for improving person re id via image captioning"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "re id done right towards good practices for person re identification"
    - "convolutional image captioning"
    - "person re identification by deep joint learning of multi loss classification"
    - "deep person learning discriminative deep features for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-5 (induced by source paper "learning discriminative features with multiple granularities for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "ring loss convex feature normalization for face recognition"
    - "re id done right towards good practices for person re identification"

Connected through hyperedge HE-6 (induced by source paper "feature affinity based pseudo labeling for semi supervised person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "improving person re identification by attribute and identity learning"
    - "weighted bilinear coding over salient body parts for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"

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)