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: "ian the individual aggregation network for person search"
  Abstract: Person search in real-world scenarios is a new challenging computer version task with many meaningful applications. The challenge of this task mainly comes from: (1) unavailable bounding boxes for pedestrians and the model needs to search for the person over the whole gallery images; (2) huge variance of visual appearance of a particular person owing to varying poses, lighting conditions, and occlusions. To address these two critical issues in modern person search applications, we propose a novel Individual Aggregation Network (IAN) that can accurately localize persons by learning to minimize intra-person feature variations. IAN is built upon the state-of-the-art object detection framework, i.e., faster R-CNN, so that high-quality region proposals for pedestrians can be produced in an online manner. In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations. The engaged center loss encourages persons with the same identity to have similar feature characteristics. Extensive experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well demonstrate the superiority of the proposed model. In particular, IAN achieves 77.23% mAP and 80.45% top-1 accuracy on CUHK-SYSU, which outperform the state-of-the-art by 1.7% and 1.85%, respectively.

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"
    - "person search via a mask guided two stream cnn model"
    - "pyramidal person re identification via multi loss dynamic training"
    - "neural person search machines"
    - "pose normalized image generation for person re identification"

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"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-3 (induced by source paper "hybrid channel based pedestrian detection"):
  with members:
    - "pcn part and context information for pedestrian detection with cnns"
    - "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation"
    - "manifold adversarial learning"
    - "fused deep neural networks for efficient pedestrian detection"
    - "exploring multi branch and high level semantic networks for improving pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "what can help pedestrian detection"
    - "learning multilayer channel features for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "reliability does matter an end to end weakly supervised semantic segmentation approach"):
  with members:
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "self produced guidance for weakly supervised object localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "efficient relaxations for dense crfs with sparse higher order potentials"

Connected through hyperedge HE-5 (induced by source paper "learning context graph for person search"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "person search via a mask guided two stream cnn model"
    - "learning human object interactions by graph parsing neural networks"
    - "video salient object detection via fully convolutional networks"
    - "videos as space time region graphs"
    - "unifying identification and context learning for person recognition"
    - "person re identification via recurrent feature aggregation"
    - "a discriminatively learned cnn embedding for person reidentification"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (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 by multi scale matching"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "simultaneous multi view instance detection with learned geometric soft constraints"):
  with members:
    - "learning pose specific representations by predicting different views"
    - "rpnet an end to end network for relative camera pose estimation"
    - "from google maps to a fine grained catalog of street trees"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "accurate deep direct geo localization from ground imagery and phone grade gps"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic discovery and geotagging of objects from street view imagery"

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)