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: "person recognition in personal photo collections"
  Abstract: People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g., backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image regions (head, body, etc.). We propose new recognition scenarios that focus on the time and appearance gap between training and testing samples. We present an in-depth analysis of the importance of different features according to time and viewpoint generalisability. In the process, we verify that our simple approach achieves the state of the art result on the PIPA  [1]  benchmark, arguably the largest social media based benchmark for person recognition to date with diverse poses, viewpoints, social groups, and events. Compared the conference version of the paper  [2]  , this paper additionally presents (1) analysis of a face recogniser (DeepID2+  [3]  ), (2) new method  naeil2  that combines the conference version method  naeil  and DeepID2+ to achieve state of the art results even compared to post-conference works, (3) discussion of related work since the conference version, (4) additional analysis including the head viewpoint-wise breakdown of performance, and (5) results on the open-world setup.

Connected through hyperedge HE-1 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "sequential attacks on agents for long term adversarial goals"
    - "a hybrid model for identity obfuscation by face replacement"

Connected through hyperedge HE-2 (induced by source paper "an end to end network for generating social relationship graphs"):
  with members:
    - "a domain based approach to social relation recognition"
    - "neural motifs scene graph parsing with global context"
    - "dual glance model for deciphering social relationships"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "gated graph sequence neural networks"
    - "spice semantic propositional image caption evaluation"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "social relation recognition in egocentric photostreams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "a domain based approach to social relation recognition"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "dockerface an easy to install and use faster r cnn face detector in a docker container"
    - "an overview of multi task learning in deep neural networks"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "a hybrid model for identity obfuscation by face replacement"):
  with members:
    - "end to end 3d face reconstruction with deep neural networks"
    - "a domain based approach to social relation recognition"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "natural and effective obfuscation by head inpainting"
    - "3d face morphable models in the wild"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "adversarial image perturbation for privacy protection a game theory perspective"

Connected through hyperedge HE-5 (induced by source paper "person search in videos with one portrait through visual and temporal links"):
  with members:
    - "unifying identification and context learning for person recognition"
    - "person search with natural language description"
    - "detecting temporally consistent objects in videos through object class label propagation"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "joint detection and identification feature learning for person search"

Connected through hyperedge HE-6 (induced by source paper "zoom rnn a novel method for person recognition using recurrent neural networks"):
  with members:
    - "pose aware person recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "faceless person recognition privacy implications in social media"
    - "sequential person recognition in photo albums with a recurrent network"
    - "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-7 (induced by source paper "query adaptive late fusion for image retrieval"):
  with members:
    - "unifying identification and context learning for person recognition"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "unifying identification and context learning for person recognition"):
  with members:
    - "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"
    - "sequential person recognition in photo albums with a recurrent network"
    - "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)