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 task mid level feature alignment network for unsupervised cross dataset person re identification"
  Abstract: Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. Such a setting severely limits their scalability in real-world applications where no labelled samples are available during the training phase. To overcome this limitation, we develop a novel unsupervised Multi-task Mid-level Feature Alignment (MMFA) network for the unsupervised cross-dataset person re-identification task. Under the assumption that the source and target datasets share the same set of mid-level semantic attributes, our proposed model can be jointly optimised under the person's identity classification and the attribute learning task with a cross-dataset mid-level feature alignment regularisation term. In this way, the learned feature representation can be better generalised from one dataset to another which further improve the person re-identification accuracy. Experimental results on four benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art baselines.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "intra camera supervised person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "weakly supervised person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "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"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-5 (induced by source paper "learning to adapt invariance in memory for person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "linkage based face clustering via graph convolution network"
    - "unsupervised tracklet person re identification"
    - "long term feature banks for detailed video understanding"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "harmonious attention network for person re identification"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-6 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-7 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-8 (induced by source paper "interpretable and generalizable person re identification with query adaptive convolution and temporal lifting"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "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"
    - "spatial temporal 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)