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: "recurrent attention models for depth based person identification"
  Abstract: We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.

Connected through hyperedge HE-1 (induced by source paper "person re identification by contour sketch under moderate clothing change"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "part aligned bilinear representations for person re identification"
    - "surveillance face recognition challenge"
    - "polar transformer networks"
    - "deformable convolutional networks"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-2 (induced by source paper "beyond intra modality a survey of heterogeneous person re identification"):
  with members:
    - "illumination adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "densely semantically aligned person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-3 (induced by source paper "action recognition using volumetric motion representations"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "unsupervised learning of view invariant action representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised completion moment detection using temporal attention"):
  with members:
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "temporal cycle consistency learning"
    - "action completion a temporal model for moment detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-5 (induced by source paper "mitigate bias in face recognition using skewness aware reinforcement learning"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal manifold learning for human motions via long horizon modeling"):
  with members:
    - "on human motion prediction using recurrent neural networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "recurrent network models for human dynamics"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-7 (induced by source paper "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"):
  with members:
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "recurrent models of visual attention"
    - "learning where to attend with deep architectures for image tracking"

Connected through hyperedge HE-8 (induced by source paper "reinforced temporal attention and split rate transfer for depth based person re identification"):
  with members:
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "person re identification via recurrent feature aggregation"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "jointly attentive spatial temporal pooling networks for video based 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)