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: "efficient and deep person re identification using multi level similarity"
  Abstract: Person Re-Identification (ReID) requires comparing two images of person captured under different conditions. Existing work based on neural networks often computes the similarity of feature maps from one single convolutional layer. In this work, we propose an efficient, end-to-end fully convolutional Siamese network that computes the similarities at multiple levels. We demonstrate that multi-level similarity can improve the accuracy considerably using low-complexity network structures in ReID problem. Specifically, first, we use several convolutional layers to extract the features of two input images. Then, we propose Convolution Similarity Network to compute the similarity score maps for the inputs. We use spatial transformer networks (STNs) to determine spatial attention. We propose to apply efficient depth-wise convolution to compute the similarity. The proposed Convolution Similarity Networks can be inserted into different convolutional layers to extract visual similarities at different levels. Furthermore, we use an improved ranking loss to further improve the performance. Our work is the first to propose to compute visual similarities at low, middle and high levels for ReID. With extensive experiments and analysis, we demonstrate that our system, compact yet effective, can achieve competitive results with much smaller model size and computational complexity.

Connected through hyperedge HE-1 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-2 (induced by source paper "scan self and collaborative attention network for video person re identification"):
  with members:
    - "person re identification in videos by analyzing spatio temporal tubes"
    - "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"
    - "person re identification by camera correlation aware feature augmentation"
    - "learning view specific deep networks for person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-3 (induced by source paper "vehicle re identification in aerial imagery dataset and approach"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "learning rich features for image manipulation detection"
    - "vision meets drones a challenge"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-4 (induced by source paper "bag of negatives for siamese architectures"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "human semantic parsing for person re identification"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "discriminative feature learning with foreground attention for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-6 (induced by source paper "deep adaptive temporal pooling for activity recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "appearance and relation networks for video classification"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-7 (induced by source paper "partial person re identification with alignment and hallucination"):
  with members:
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "camera style adaptation for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"

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)