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: "group membership prediction"
  Abstract: The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "a spatial and temporal features mixture model with body parts for video based person re identification"):
  with members:
    - "person re identification via recurrent feature aggregation"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "a siamese long short term memory architecture for human re identification"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "learning to rank in person re identification with metric ensembles"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "recurrent models of visual attention"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-2 (induced by source paper "a scalable pipelined dataflow accelerator for object region proposals on fpga platform"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "ssd single shot multibox detector"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "sequential optimization for efficient high quality object proposal generation"

Connected through hyperedge HE-3 (induced by source paper "looking beyond appearances synthetic training data for deep cnns in re identification"):
  with members:
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "personnet person re identification with deep convolutional neural networks"
    - "a siamese long short term memory architecture for human re identification"
    - "a multi task deep network for person re identification"
    - "multiregion bilinear convolutional neural networks for person re identification"

Connected through hyperedge HE-4 (induced by source paper "three stream convolutional networks for video based person re identification"):
  with members:
    - "video based person re identification with accumulative motion context"
    - "end to end comparative attention networks for person re identification"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "personnet person re identification with deep convolutional neural networks"
    - "convolutional two stream network fusion for video action recognition"
    - "end to end deep learning for person search"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "jointly attentive spatial temporal pooling networks for video based person re identification"):
  with members:
    - "person re identification via recurrent feature aggregation"
    - "end to end comparative attention networks for person re identification"
    - "attentive pooling networks"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "top push video based person re identification"
    - "learning to rank in person re identification with metric ensembles"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "action recognition using visual attention"

Connected through hyperedge HE-6 (induced by source paper "a domain based approach to social relation recognition"):
  with members:
    - "person recognition in personal photo collections"
    - "visual translation embedding network for visual relation detection"
    - "visual relationship detection with language priors"
    - "beyond frontal faces improving person recognition using multiple cues"
    - "appearance based gaze estimation in the wild"
    - "learning social relation traits from face images"
    - "joint inference of groups events and human roles in aerial videos"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "group re identification via unsupervised transfer of sparse features encoding"):
  with members:
    - "learning a discriminative null space for person re identification"
    - "identity mappings in deep residual networks"
    - "learning to rank in person re identification with metric ensembles"
    - "deep residual learning for image recognition"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "high speed tracking with kernelized correlation filters"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "re identification of humans in crowds using personal social and environmental constraints"):
  with members:
    - "egocentric meets surveillance"
    - "ego2top matching viewers in egocentric and top view videos"
    - "very deep convolutional networks for large scale image recognition"
    - "person re identification by local maximal occurrence representation and metric learning"

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)