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: "cross domain visual matching via generalized similarity measure and feature learning"
  Abstract: Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "unsupervised concatenation hashing via combining subspace learning and graph embedding for cross modal image retrieval"):
  with members:
    - "learning discriminative hashing codes for cross modal retrieval based on multi view features"
    - "semi supervised hashing for semi paired cross view retrieval"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "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"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-3 (induced by source paper "scan self and collaborative attention network for video person re identification"):
  with members:
    - "person re identification in videos by analyzing spatio temporal tubes"
    - "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"
    - "person re identification by camera correlation aware feature augmentation"
    - "learning view specific deep networks for person re identification"
    - "harmonious attention network for person re identification"
    - "efficient and deep person re identification using multi level similarity"

Connected through hyperedge HE-4 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "spatial temporal person re identification"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "graph based global reasoning networks"

Connected through hyperedge HE-5 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "illumination adaptive person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "gait recognition via disentangled representation learning"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "re identification with consistent attentive siamese networks"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "hetero center loss for cross modality person re identification"):
  with members:
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "alignedreid surpassing human level performance in person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "deep person learning discriminative deep features for person re identification"

Connected through hyperedge HE-7 (induced by source paper "attend to the difference cross modality person re identification via contrastive correlation"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "vrstc occlusion free video person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "person transfer gan to bridge domain gap for person re identification"
    - "attention based natural language person retrieval"

Connected through hyperedge HE-8 (induced by source paper "instance aware representation learning and association for online multi person tracking"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "learning by tracking siamese cnn for robust target association"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "training region based object detectors with online hard example mining"
    - "performance measures and a data set for multi target multi camera tracking"
    - "fast r cnn"

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)