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: "detecting gaze towards eyes in natural social interactions and its use in child assessment"
  Abstract: Eye contact is a crucial element of non-verbal communication that signifies interest, attention, and participation in social interactions. As a result, measures of eye contact arise in a variety of applications such as the assessment of the social communication skills of children at risk for developmental disorders such as autism, or the analysis of turn-taking and social roles during group meetings. However, the automated measurement of visual attention during naturalistic social interactions is challenging due to the difficulty of estimating a subject's looking direction from video. This paper proposes a novel approach to eye contact detection during adult-child social interactions in which the adult wears a point-of-view camera which captures an egocentric view of the child's behavior. By analyzing the child's face regions and inferring their head pose we can accurately identify the onset and duration of the child's looks to their social partner's eyes. We introduce the Pose-Implicit CNN, a novel deep learning architecture that predicts eye contact while implicitly estimating the head pose. We present a fully automated system for eye contact detection that solves the sub-problems of end-to-end feature learning and pose estimation using deep neural networks. To train our models, we use a dataset comprising 22 hours of 156 play session videos from over 100 children, half of whom are diagnosed with Autism Spectrum Disorder. We report an overall precision of 0.76, recall of 0.80, and an area under the precision-recall curve of 0.79, all of which are significant improvements over existing methods.

Connected through hyperedge HE-1 (induced by source paper "detecting attended visual targets in video"):
  with members:
    - "laeo net revisiting people looking at each other in videos"
    - "extended gaze following detecting objects in videos beyond the camera field of view"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "facial pose estimation by deep learning from label distributions"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "fine grained head pose estimation without keypoints"
    - "deep label distribution learning with label ambiguity"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"

Connected through hyperedge HE-3 (induced by source paper "prediction of gaze direction using convolutional neural networks for autism diagnosis"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"):
  with members:
    - "eye tracking for everyone"
    - "subjects and their objects localizing interactees for a person centric view of importance"
    - "it s written all over your face full face appearance based gaze estimation"
    - "visual saliency based on multiscale deep features"
    - "deep residual learning for image recognition"
    - "salient object detection a benchmark"
    - "appearance based gaze estimation in the wild"
    - "rendering of eyes for eye shape registration and gaze estimation"

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.HC(Human-Computer Interaction)