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: "multi level factorisation net for person re identification"
  Abstract: Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes. We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.

Connected through hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "learning to adapt structured output space for semantic segmentation"
    - "harmonious attention network for person re identification"

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"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"

Connected through hyperedge HE-3 (induced by source paper "learning shape representations for clothing variations in person re identification"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "rgb infrared cross modality person re identification via joint pixel and feature alignment"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "deep representation learning with part loss for person re identification"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-4 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-5 (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"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"

Connected through hyperedge HE-6 (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"
    - "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"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-7 (induced by source paper "unity style transfer for person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "spatial temporal person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "improving shape deformation in unsupervised image to image translation"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-8 (induced by source paper "multi task learning with coarse priors for robust part aware person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "cdpm convolutional deformable part models for person re identification"
    - "relation aware global attention"
    - "discriminative feature learning with foreground attention for person re identification"
    - "attention aware compositional network for person re identification"
    - "factorized distillation training holistic person re identification model by distilling an ensemble of partial reid models"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "in defense of the triplet loss 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)