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: "vip finding important people in images"
  Abstract: People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This paper analyzes the concept of the importance of individuals in group photographs. We address two specific questions -- Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in significant improvement in applications such as im2text (generating sentences that describe images of groups of people).

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 "learning to learn relation for important people detection in still images"):
  with members:
    - "relational deep reinforcement learning"
    - "latent embeddings for collective activity recognition"
    - "personrank detecting important people in images"
    - "attention is all you need"
    - "relation networks for object detection"
    - "learning to compare relation network for few shot learning"
    - "massive exploration of neural machine translation architectures"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "personrank detecting important people in images"):
  with members:
    - "ssd single shot multibox detector"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "human social interaction modeling using temporal deep networks"
    - "reconciling saliency and object center bias hypotheses in explaining free viewing fixations"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "detecting events and key actors in multi person videos"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-4 (induced by source paper "sequential person recognition in photo albums with a recurrent network"):
  with members:
    - "person recognition in personal photo collections"
    - "faceless person recognition privacy implications in social media"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "image captioning and visual question answering based on attributes and their related external knowledge"
    - "cnn rnn a unified framework for multi label image classification"
    - "chained predictions using convolutional neural networks"
    - "fvqa fact based visual question answering"
    - "ask me anything free form visual question answering based on knowledge from external sources"

Connected through hyperedge HE-5 (induced by source paper "faceless person recognition privacy implications in social media"):
  with members:
    - "person recognition in personal photo collections"
    - "personnet person re identification with deep convolutional neural networks"
    - "can we still avoid automatic face detection"
    - "context aware cnns for person head detection"
    - "facenet a unified embedding for face recognition and clustering"
    - "beyond frontal faces improving person recognition using multiple cues"
    - "a comprehensive survey on pose invariant face recognition"
    - "naive deep face recognition touching the limit of lfw benchmark or not"

Connected through hyperedge HE-6 (induced by source paper "cloudcv large scale distributed computer vision as a cloud service"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "theano new features and speed improvements"
    - "building high level features using large scale unsupervised 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)