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 re identification in identity regression space"
  Abstract: Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an Identity Regression Space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.

Connected through hyperedge HE-1 (induced by source paper "progressive darts bridging the optimization gap for nas in the wild"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "centernet keypoint triplets for object detection"
    - "searching for a robust neural architecture in four gpu hours"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "stabilizing darts with amended gradient estimation on architectural parameters"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "cross view kernel similarity metric learning using pairwise constraints for person re identification"):
  with members:
    - "multiple kernel fisher discriminant metric learning for person re identification"
    - "person re identification with metric learning using privileged information"
    - "a semi supervised maximum margin metric learning approach for small scale person re identification"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "graph correspondence transfer for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-3 (induced by source paper "a semi supervised maximum margin metric learning approach for small scale person re identification"):
  with members:
    - "end to end deep kronecker product matching for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "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"
    - "deep group shuffling random walk for person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "person search by multi scale matching"):
  with members:
    - "multi level factorisation net for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"
    - "an implementation of faster rcnn with study for region sampling"

Connected through hyperedge HE-5 (induced by source paper "unsupervised person re identification by deep learning tracklet association"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "quality aware network for set to set recognition"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-6 (induced by source paper "vehicle re identification in context"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention 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"
    - "unsupervised person re identification by deep learning tracklet association"
    - "camera style adaptation for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)