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: "re id done right towards good practices for person re identification"
  Abstract: Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.

Connected through hyperedge HE-1 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification"):
  with members:
    - "view confusion feature learning for person re identification"
    - "resolution invariant person re identification"
    - "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"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"

Connected through hyperedge HE-2 (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"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "semantics aligned representation learning for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "re identification supervised texture generation"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "relation aware global attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "residual non local attention networks for image restoration"
    - "learning discriminative features with multiple granularities for person re identification"
    - "ocnet object context network for scene parsing"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "recognizing partial biometric patterns"

Connected through hyperedge HE-5 (induced by source paper "progressive learning algorithm for efficient person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "harmonious attention network for person re identification"
    - "pyramid person matching network for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-6 (induced by source paper "local features and visual words emerge in activations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "neighbourhood consensus networks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "lift learned invariant feature transform"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-7 (induced by source paper "aanet attribute attention network for person re identifications"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "semantic correlation promoted shape variant context for segmentation"
    - "feature mask network for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "end to end deep kronecker product matching for person re identification"
    - "human semantic parsing for person re identification"
    - "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-8 (induced by source paper "stripe based and attribute aware network a two branch deep model for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "attributes guided feature learning for vehicle re identification"
    - "vehicle re identification in aerial imagery dataset and approach"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "vehicle re identification using quadruple directional deep learning features"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "part based deep hashing for large scale 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)