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: "video person re identification by temporal residual learning"
  Abstract: In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal information of video sequences and tackle the poor spatial alignment of moving pedestrians. More specifically, for exploiting the temporal information, we design a temporal residual learning (TRL) module to simultaneously extract the generic and specific features of consecutive frames. The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are, respectively, responsible to describe a moving person in different aspects, providing complementary information for better feature representations. To deal with the poor spatial alignment in video re-ID data sets, we propose a spatial-temporal transformer network (ST2N) module. Transformation parameters in the ST2N module are learned by leveraging the high-level semantic information of the current frame as well as the temporal context knowledge from other frames. The proposed ST2N module with less learnable parameters allows effective person alignments under significant appearance changes. Extensive experimental results on the large-scale MARS, PRID2011, ILIDS-VID, and SDU-VID data sets demonstrate that the proposed method achieves consistently superior performance and outperforms most of the very recent state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "person re identification with metric learning using privileged information"
    - "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"
    - "learning view specific deep networks for person re identification"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"

Connected through hyperedge HE-2 (induced by source paper "adaptive graph representation learning for video person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interaction and aggregation network for person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning context graph for person search"
    - "omni scale feature learning for person re identification"
    - "scan self and collaborative attention network for video person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "spatially and temporally efficient non local attention network for video based person re identification"

Connected through hyperedge HE-3 (induced by source paper "adaptive semantic visual tree for hierarchical embeddings"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "attribute aware attention model for fine grained representation learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "deep metric learning with hierarchical triplet loss"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "spatial temporal synergic residual learning for video person re identification"):
  with members:
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "three stream convolutional networks for video based person re identification"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "person re identification past present and future"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "spatiotemporal residual networks for video action recognition"

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)