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 by multi scale matching"
  Abstract: We consider the problem of person search in unconstrained scene images. Existing methods usually focus on improving the person detection accuracy to mitigate negative effects imposed by misalignment, mis-detections, and false alarms resulted from noisy people auto-detection. In contrast to previous studies, we show that sufficiently reliable person instance cropping is achievable by slightly improved state-of-the-art deep learning object detectors (e.g. Faster-RCNN), and the under-studied multi-scale matching problem in person search is a more severe barrier. In this work, we address this multi-scale person search challenge by proposing a Cross-Level Semantic Alignment (CLSA) deep learning approach capable of learning more discriminative identity feature representations in a unified end-to-end model. This is realised by exploiting the in-network feature pyramid structure of a deep neural network enhanced by a novel cross pyramid-level semantic alignment loss function. This favourably eliminates the need for constructing a computationally expensive image pyramid and a complex multi-branch network architecture. Extensive experiments show the modelling advantages and performance superiority of CLSA over the state-of-the-art person search and multi-scale matching methods on two large person search benchmarking datasets: CUHK-SYSU and PRW.

Connected through hyperedge HE-1 (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"
    - "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"
    - "person search via a mask guided two stream cnn model"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-2 (induced by source paper "rgpnet a real time general purpose semantic segmentation"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "fast scnn fast semantic segmentation network"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "contextnet exploring context and detail for semantic segmentation in real time"

Connected through hyperedge HE-3 (induced by source paper "re id driven localization refinement for person search"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "person search via a mask guided two stream cnn model"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "focal loss for dense object detection"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "neural person search machines"

Connected through hyperedge HE-4 (induced by source paper "progressive unsupervised person re identification by tracklet association with spatio temporal regularization"):
  with members:
    - "unsupervised person re identification by soft multilabel learning"
    - "pose flow efficient online pose tracking"
    - "harmonious attention network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "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"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-5 (induced by source paper "self referenced deep learning"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "knowledge distillation by on the fly native ensemble"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"
    - "snapshot ensembles train 1 get m for free"
    - "person re identification by deep joint learning of multi loss classification"
    - "deep reinforcement learning attention selection for person re identification"

Connected through hyperedge HE-6 (induced by source paper "fast human pose estimation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "self referenced deep learning"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "learning feature pyramids for human pose estimation"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "knowledge distillation by on the fly native ensemble"):
  with members:
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "squeeze and excitation networks"
    - "condensenet an efficient densenet using learned group convolutions"
    - "deep networks with stochastic depth"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "densely connected convolutional networks"

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)