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: "sequential person recognition in photo albums with a recurrent network"
  Abstract: Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.

Connected through hyperedge HE-1 (induced by source paper "person recognition in personal photo collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "pose aware person recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "robust depth based person re identification"
    - "level playing field for million scale face recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-3 (induced by source paper "improved hard example mining by discovering attribute based hard person identity"):
  with members:
    - "fmt fusing multi task convolutional neural network for person search"
    - "pedestrian attribute recognition a survey"
    - "auto reid searching for a part aware convnet for person re identification"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-4 (induced by source paper "actor critic instance segmentation"):
  with members:
    - "lstm a search space odyssey"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "spatial memory for context reasoning in object detection"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "bottom up instance segmentation using deep higher order crfs"
    - "end to end instance segmentation and counting with recurrent attention"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive overview of biometric fusion"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-6 (induced by source paper "attribute aware pooling for pedestrian attribute recognition"):
  with members:
    - "attribute aware attention model for fine grained representation learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "localization guided learning for pedestrian attribute recognition"
    - "autoencoder inspired unsupervised feature selection"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"

Connected through hyperedge HE-7 (induced by source paper "zoom rnn a novel method for person recognition using recurrent neural networks"):
  with members:
    - "person recognition in personal photo collections"
    - "pose aware person recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "faceless person recognition privacy implications in social media"
    - "facenet a unified embedding for face recognition and clustering"
    - "beyond frontal faces improving person recognition using multiple cues"
    - "rethinking the inception architecture for computer vision"
    - "end to end people detection in crowded scenes"

Connected through hyperedge HE-8 (induced by source paper "unifying identification and context learning for person recognition"):
  with members:
    - "person recognition in personal photo collections"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "joint detection and identification feature learning for person search"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)