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: "evaluation of appearance based methods and implications for gaze based applications"
  Abstract: Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.

Connected through hyperedge HE-1 (induced by source paper "few shot adaptive gaze estimation"):
  with members:
    - "on first order meta learning algorithms"
    - "few shot segmentation propagation with guided networks"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "light weight head pose invariant gaze tracking"
    - "deep pictorial gaze estimation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (induced by source paper "a generalized and robust method towards practical gaze estimation on smart phone"):
  with members:
    - "few shot adaptive gaze estimation"
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "snapshot distillation teacher student optimization in one generation"
    - "feature denoising for improving adversarial robustness"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "repr improved training of convolutional filters"

Connected through hyperedge HE-3 (induced by source paper "accurate and robust eye contact detection during everyday mobile device interactions"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "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"

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)