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: "disjoint label space transfer learning with common factorised space"
  Abstract: In this paper, a unified approach is presented to transfer learning that addresses several source and target domain label-space and annotation assumptions with a single model. It is particularly effective in handling a challenging case, where source and target label-spaces are disjoint, and outperforms alternatives in both unsupervised and semi-supervised settings. The key ingredient is a common representation termed Common Factorised Space. It is shared between source and target domains, and trained with an unsupervised factorisation loss and a graph-based loss. With a wide range of experiments, we demonstrate the flexibility, relevance and efficacy of our method, both in the challenging cases with disjoint label spaces, and in the more conventional cases such as unsupervised domain adaptation, where the source and target domains share the same label-sets.

Connected through hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"):
  with members:
    - "deep self learning from noisy labels"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "when does label smoothing help"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-2 (induced by source paper "structured domain adaptation for unsupervised person re identification"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "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"
    - "bidirectional learning for domain adaptation of semantic segmentation"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "cross dataset person re identification via unsupervised pose disentanglement and adaptation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "cgans with projection discriminator"
    - "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-5 (induced by source paper "frustratingly easy person re identification generalizing person re id in practice"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "batch instance normalization for adaptively style invariant neural networks"

Connected through hyperedge HE-6 (induced by source paper "interpretable and generalizable deep image matching with adaptive convolutions"):
  with members:
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "unsupervised domain adaptive re identification theory and practice"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"

Connected through hyperedge HE-7 (induced by source paper "asymmetric co teaching for unsupervised cross domain person re identification"):
  with members:
    - "learning to adapt invariance in memory for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "unsupervised domain adaptive re identification theory and practice"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "leveraging virtual and real person for unsupervised 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)