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: "attention aware compositional network for person re identification"
  Abstract: Person re-identification (ReID) is to identify pedestrians observed from different camera views based on visual appearance. It is a challenging task due to large pose variations, complex background clutters and severe occlusions. Recently, human pose estimation by predicting joint locations was largely improved in accuracy. It is reasonable to use pose estimation results for handling pose variations and background clutters, and such attempts have obtained great improvement in ReID performance. However, we argue that the pose information was not well utilized and hasn't yet been fully exploited for person ReID. #R##N#In this work, we introduce a novel framework called Attention-Aware Compositional Network (AACN) for person ReID. AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA is learned and applied to mask out undesirable background features in pedestrian feature maps. Furthermore, pose-guided visibility scores are estimated for body parts to deal with part occlusion in the proposed AFC module. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID, CUHK03-NP and DukeMTMC-reID.

Connected through hyperedge HE-1 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-2 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation"):
  with members:
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "video action transformer network"
    - "temporal recurrent networks for online action detection"
    - "attention driven person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "online action detection"

Connected through hyperedge HE-4 (induced by source paper "deep representation learning on long tailed data a learnable embedding augmentation perspective"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "class balanced loss based on effective number of samples"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "harmonious attention network for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "gradient harmonized single stage detector"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-5 (induced by source paper "diversity achieving slow dropblock network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-6 (induced by source paper "deep attention aware feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "aanet attribute attention network for person re identifications"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-7 (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"
    - "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"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-8 (induced by source paper "person re identification by contour sketch under moderate clothing change"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "harmonious attention network for person re identification"
    - "part aligned bilinear representations for person re identification"
    - "surveillance face recognition challenge"
    - "polar transformer networks"
    - "deformable convolutional networks"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "robust depth based 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)