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: "chalearn looking at people a review of events and resources"
  Abstract: This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities.

Connected through hyperedge HE-1 (induced by source paper "diversity in faces"):
  with members:
    - "the devil of face recognition is in the noise"
    - "women also snowboard overcoming bias in captioning models"
    - "every smile is unique landmark guided diverse smile generation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"
    - "bias mitigation post processing for individual and group fairness"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "an overview of computational approaches for interpretation analysis"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "adversarial defense based on structure to signal autoencoders"
    - "what do deep networks like to see"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-3 (induced by source paper "explaining first impressions modeling recognizing and explaining apparent personality from videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "generating visual explanations"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-4 (induced by source paper "inclusivefacenet improving face attribute detection with race and gender diversity"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "using deep learning and google street view to estimate the demographic makeup of the us"
    - "equality of opportunity in supervised learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "an all in one convolutional neural network for face analysis"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "heterogeneous face attribute estimation a deep multi task learning approach"):
  with members:
    - "attributes for improved attributes a multi task network for attribute classification"
    - "face attribute prediction using off the shelf cnn features"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi task cnn model for attribute prediction"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "the unconstrained ear recognition challenge"):
  with members:
    - "pixel wise ear detection with convolutional encoder decoder networks"
    - "training convolutional neural networks with limited training data for ear recognition in the wild"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ear recognition more than a survey"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)