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: "parameter free spatial attention network for person re identification"
  Abstract: Global average pooling (GAP) allows to localize discriminative information for recognition [40]. While GAP helps the convolution neural network to attend to the most discriminative features of an object, it may suffer if that information is missing e.g. due to camera viewpoint changes. To circumvent this issue, we argue that it is advantageous to attend to the global configuration of the object by modeling spatial relations among high-level features. We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model. Our spatial attention layer consistently improves the performance over the model without it. Results on four benchmarks demonstrate a superiority of our model over the state-of-the-art achieving rank-1 accuracy of 94.7% on Market-1501, 89.0% on DukeMTMC-ReID, 74.9% on CUHK03-labeled and 69.7% on CUHK03-detected.

Connected through hyperedge HE-1 (induced by source paper "iterative and adaptive sampling with spatial attention for black box model explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "faceforensics learning to detect manipulated facial images"
    - "embedding human knowledge in deep neural network via attention map"
    - "why do these match explaining the behavior of image similarity models"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "argan attentive recurrent generative adversarial network for shadow detection and removal"
    - "visualizing deep networks by optimizing with integrated gradients"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial perturbations against person re identification"):
  with members:
    - "unsupervised adversarial attacks on deep feature based retrieval with gan"
    - "joint discriminative and generative learning for person re identification"
    - "learning with average precision training image retrieval with a listwise loss"
    - "metric attack and defense for person re identification"
    - "adversarial metric attack for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "re identification with consistent attentive siamese networks"

Connected through hyperedge HE-3 (induced by source paper "calibrated domain invariant learning for highly generalizable large scale re identification"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "abd net attentive but diverse person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "ram a region aware deep model for vehicle re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "focal loss for dense object detection"

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)