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: "end to end deep learning for person search"
  Abstract: Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. To close the gap, we investigate how to localize and match query persons from the scene images without relying on the annotations of candidate boxes. Instead of breaking it down into two separate tasks—pedestrian detection and person re-id, we propose an end-to-end deep learning framework to jointly handle both tasks. A random sampling softmax loss is proposed to effectively train the model under the supervision of sparse and unbalanced labels. On the other hand, existing benchmarks are small in scale and the samples are collected from a few fixed camera views with low scene diversities. To address this issue, we collect a largescale and scene-diversified person search dataset, which contains 18,184 images, 8,432 persons, and 99,809 annotated bounding boxes1. We evaluate our approach and other baselines on the proposed dataset, and study the influence of various factors. Experiments show that our method achieves the best result.

Connected through hyperedge HE-1 (induced by source paper "a convolutional baseline for person re identification using vision and language descriptions"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "attention aware compositional network for person re identification"
    - "stacked cross attention for image text matching"
    - "pose guided joint global and attentive local matching network for text based person search"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "person search with natural language description"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "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"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-3 (induced by source paper "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "exploiting offset guided network for pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint flow temporal flow fields for multi person tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"

Connected through hyperedge HE-4 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

Connected through hyperedge HE-5 (induced by source paper "frustratingly easy person re identification generalizing person re id in practice"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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"
    - "batch instance normalization for adaptively style invariant neural networks"

Connected through hyperedge HE-6 (induced by source paper "in defense of the triplet loss again learning robust person re identification with fast approximated triplet loss and label distillation"):
  with members:
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "calibrated domain invariant learning for highly generalizable large scale re identification"
    - "the devil of face recognition is in the noise"
    - "not all samples are created equal deep learning with importance sampling"
    - "support neighbor loss for person re identification"
    - "modality distillation with multiple stream networks for action recognition"
    - "iterative learning with open set noisy labels"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-7 (induced by source paper "re identification supervised texture generation"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning category specific mesh reconstruction from image collections"
    - "harmonious attention network for person re identification"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "dense pose transfer"

Connected through hyperedge HE-8 (induced by source paper "segmentation mask guided end to end person search"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "query guided end to end person search"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "neural person search machines"
    - "end to end comparative attention networks 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)