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 based person re identification via 3d convolutional networks and non local attention"
  Abstract: Video-based person re-identification (ReID) is a challenging problem, where some video tracks of people across non-overlapping cameras are available for matching. Feature aggregation from a video track is a key step for video-based person ReID. Many existing methods tackle this problem by average/maximum temporal pooling or RNNs with attention. However, these methods cannot deal with temporal dependency and spatial misalignment problems at the same time. We are inspired by video action recognition that involves the identification of different actions from video tracks. Firstly, we use 3D convolutions on video volume, instead of using 2D convolutions across frames, to extract spatial and temporal features simultaneously. Secondly, we use a non-local block to tackle the misalignment problem and capture spatial-temporal long-range dependencies. As a result, the network can learn useful spatial-temporal information as a weighted sum of the features in all space and temporal positions in the input feature map. Experimental results on three datasets show that our framework outperforms state-of-the-art approaches by a large margin on multiple metrics.

Connected through hyperedge HE-1 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "beyond human parts dual part aligned representations for person re identification"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "aanet attribute attention network for person re identifications"
    - "object contextual representations for semantic segmentation"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "multi level factorisation net for person re identification"

Connected through hyperedge HE-3 (induced by source paper "a spectral nonlocal block for neural networks"):
  with members:
    - "compact global descriptor for neural networks"
    - "lightweight network architecture for real time action recognition"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "scan self and collaborative attention network for video person re identification"
    - "a comprehensive survey on graph neural networks"
    - "stochastic trajectory prediction with social graph network"
    - "ccnet criss cross attention for semantic segmentation"
    - "compact generalized non local network"

Connected through hyperedge HE-4 (induced by source paper "rethinking temporal fusion for video based person re identification on semantic and time aspect"):
  with members:
    - "operator in the loop deep sequential multi camera feature fusion for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "revisiting temporal modeling for video based person reid"
    - "person re identification with fusion of hand crafted and deep pose based body region features"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "intra clip aggregation for video person re identification"):
  with members:
    - "spatial temporal synergic residual learning for video person re identification"
    - "revisiting temporal modeling for video based person reid"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-6 (induced by source paper "efficient attention attention with linear complexities"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "learning depth with convolutional spatial propagation network"
    - "yolov3 an incremental improvement"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "non local recurrent network for image restoration"
    - "large scale gan training for high fidelity natural image synthesis"

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)