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: "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
  Abstract: Visual attention is highly fragmented during mobile interactions, but the erratic nature of attention shifts currently limits attentive user interfaces to adapting after the fact, i.e. after shifts have already happened. We instead study attention forecasting - the challenging task of predicting users' gaze behaviour (overt visual attention) in the near future. We present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). We propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate that our method can forecast bidirectional attention shifts and predict whether the primary attentional focus is on the handheld mobile device. We study the impact of different feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.

Connected through hyperedge HE-1 (induced by source paper "evaluation of appearance based methods and implications for gaze based applications"):
  with members:
    - "detecting low rapport during natural interactions in small groups from non verbal behaviour"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"
    - "prediction of search targets from fixations in open world settings"

Connected through hyperedge HE-2 (induced by source paper "accurate and robust eye contact detection during everyday mobile device interactions"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "light weight head pose invariant gaze tracking"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "how far are we from quantifying visual attention in mobile hci"):
  with members:
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-4 (induced by source paper "privaceye privacy preserving head mounted eye tracking using egocentric scene image and eye movement features"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "going deeper with convolutions"
    - "screenavoider protecting computer screens from ubiquitous cameras"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-5 (induced by source paper "teacher s perception in the classroom"):
  with members:
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "visual decoding of targets during visual search from human eye fixations"
    - "predicting the category and attributes of visual search targets using deep gaze pooling"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "subjective annotations for vision based attention level estimation"):
  with members:
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "poseidon face from depth for driver pose 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)