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: "enhancing person re identification in a self trained subspace"
  Abstract: Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully-supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully-supervised re-ID is the limited availability of labeled training samples. To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo pairwise relationships iteratively, which further improves the learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in person's appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely-used datasets demonstrate the effectiveness of our approach and its performance can be comparable to the reported results of most state-of-the-art fully-supervised methods while using much fewer labeled data.

Connected through hyperedge HE-1 (induced by source paper "person re identification with metric learning using privileged information"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "temporal model adaptation for person re identification"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-2 (induced by source paper "who where and what to wear extracting fashion knowledge from social media"):
  with members:
    - "low resource name tagging learned with weakly labeled data"
    - "kgat knowledge graph attention network for recommendation"
    - "person re identification with metric learning using privileged information"
    - "yolov3 an incremental improvement"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "joint optimization framework for learning with noisy labels"
    - "aesthetic based clothing recommendation"
    - "transnfcm translation based neural fashion compatibility modeling"

Connected through hyperedge HE-3 (induced by source paper "manifold criterion guided transfer learning via intermediate domain generation"):
  with members:
    - "domain adaptation for visual applications a comprehensive survey"
    - "adversarial discriminative domain adaptation"
    - "unsupervised domain adaptation with residual transfer networks"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "self learning camera autonomous adaptation of object detectors to unlabeled video streams"

Connected through hyperedge HE-4 (induced by source paper "optimal projection guided transfer hashing for image retrieval"):
  with members:
    - "deep hashing network for unsupervised domain adaptation"
    - "a survey on learning to hash"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "domain generalization via conditional invariant representation"):
  with members:
    - "deep transfer learning with joint adaptation networks"
    - "domain generalization for object recognition with multi task autoencoders"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "on causal and anticausal learning"

Connected through hyperedge HE-6 (induced by source paper "pedestrian alignment network for large scale person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "divide and fuse a re ranking approach for person re identification"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-7 (induced by source paper "a discriminatively learned cnn embedding for person reidentification"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "multichannel kernel canonical correlation analysis for cross view person reidentification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera 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)