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: "person search via a mask guided two stream cnn model"
  Abstract: In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID). Instead of sharing representations in a single joint model, we find that separating detector and re-ID feature extraction yields better performance. In order to extract more representative features for each identity, we segment out the foreground person from the original image patch. We propose a simple yet effective re-ID method, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams. From the experiments on two standard person search benchmarks of CUHK-SYSU and PRW, we achieve mAP of $83.0\%$ and $32.6\%$ respectively, surpassing the state of the art by a large margin (more than 5pp).

Connected through hyperedge HE-1 (induced by source paper "triplet online instance matching loss for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "query guided end to end person search"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "neural person search machines"
    - "pose normalized image generation for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "push for center learning via orthogonalization and subspace masking for person re identification"):
  with members:
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "simple baselines for human pose estimation and tracking"
    - "horizontal pyramid matching for person re identification"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-3 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-4 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "query guided end to end person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-5 (induced by source paper "selective kernel networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bam bottleneck attention module"
    - "residual networks of residual networks multilevel residual networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-6 (induced by source paper "spatial group wise enhance improving semantic feature learning in convolutional networks"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "searching for mobilenetv3"
    - "selective kernel networks"
    - "weight standardization"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-7 (induced by source paper "unified multifaceted feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interaction and aggregation network for person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "joint discriminative and generative learning for person re identification"
    - "deep representation learning with part loss for person re identification"
    - "abd net attentive but diverse person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"

Connected through hyperedge HE-8 (induced by source paper "multi scale body part mask guided attention for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "attention aware compositional network for person re identification"
    - "horizontal pyramid matching 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)