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: "continuous adaptation of multi camera person identification models through sparse non redundant representative selection"
  Abstract: The problem of image-base person identification/recognition is to provide an identity to the image of an individual based on learned models that describe his/her appearance. Most traditional person identification systems rely on learning a static model on tediously labeled training data. Though labeling manually is an indispensable part of a supervised framework, for a large scale identification system labeling huge amount of data is a significant overhead. For large multi-sensor data as typically encountered in camera networks, labeling a lot of samples does not always mean more information, as redundant images are labeled several times. In this work, we propose a convex optimization based iterative framework that progressively and judiciously chooses a sparse but informative set of samples for labeling, with minimal overlap with previously labeled images. We also use a structure preserving sparse reconstruction based classifier to reduce the training burden typically seen in discriminative classifiers. The two stage approach leads to a novel framework for online update of the classifiers involving only the incorporation of new labeled data rather than any expensive training phase. We demonstrate the effectiveness of our approach on multi-camera person re-identification datasets, to demonstrate the feasibility of learning online classification models in multi-camera big data applications. Using three benchmark datasets, we validate our approach and demonstrate that our framework achieves superior performance with significantly less amount of manual labeling.

Connected through hyperedge HE-1 (induced by source paper "deep adaptive feature embedding with local sample distributions for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "hcp a flexible cnn framework for multi label image classification"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "embedding deep metric for person re identication a study against large variations"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-2 (induced by source paper "where and when to look deep siamese attention networks for video based person re identification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "svdnet for pedestrian retrieval"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "deep transfer learning for person re identification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised adaptive re identification in open world dynamic camera networks"):
  with members:
    - "person re identification via recurrent feature aggregation"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "personnet person re identification with deep convolutional neural networks"
    - "person re identification past present and future"
    - "end to end deep learning for person search"
    - "human in the loop person re identification"
    - "person re identification by discriminative selection in video ranking"

Connected through hyperedge HE-4 (induced by source paper "multi shot pedestrian re identification via sequential decision making"):
  with members:
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "person re identification by deep joint learning of multi loss classification"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)