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: "harnessing the web for population scale physiological sensing a case study of sleep and performance"
  Abstract: Human cognitive performance is critical to productivity, learning, and accident avoidance. Cognitive performance varies throughout each day and is in part driven by intrinsic, near 24-hour circadian rhythms. Prior research on the impact of sleep and circadian rhythms on cognitive performance has typically been restricted to small-scale laboratory-based studies that do not capture the variability of real-world conditions, such as environmental factors, motivation, and sleep patterns in real-world settings. Given these limitations, leading sleep researchers have called for larger in situ monitoring of sleep and performance. We present the largest study to date on the impact of objectively measured real-world sleep on performance enabled through a reframing of everyday interactions with a web search engine as a series of performance tasks. Our analysis includes 3 million nights of sleep and 75 million interaction tasks. We measure cognitive performance through the speed of keystroke and click interactions on a web search engine and correlate them to wearable device-defined sleep measures over time. We demonstrate that real-world performance varies throughout the day and is influenced by both circadian rhythms, chronotype (morning/evening preference), and prior sleep duration and timing. We develop a statistical model that operationalizes a large body of work on sleep and performance and demonstrates that our estimates of circadian rhythms, homeostatic sleep drive, and sleep inertia align with expectations from laboratory-based sleep studies. Further, we quantify the impact of insufficient sleep on real-world performance and show that two consecutive nights with less than six hours of sleep are associated with decreases in performance which last for a period of six days. This work demonstrates the feasibility of using online interactions for large-scale physiological sensing.

Connected through hyperedge HE-1 (induced by source paper "capturing evolution genes for time series data"):
  with members:
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "modeling interdependent and periodic real world action sequences"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-2 (induced by source paper "adversarial unsupervised representation learning for activity time series"):
  with members:
    - "sleep quality prediction from wearable data using deep learning"
    - "node2vec scalable feature learning for networks"
    - "deep activity recognition models with triaxial accelerometers"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "modeling interdependent and periodic real world action sequences"):
  with members:
    - "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-4 (induced by source paper "learning individualized cardiovascular responses from large scale wearable sensors data"):
  with members:
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"
    - "attention is all you need"
    - "xgboost a scalable tree boosting system"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "need for sleep the impact of a night of sleep deprivation on novice developers performance"):
  with members:
    - "abnormal working hours effect of rapid releases and implications to work content"
    - "towards the assessment of stress and emotional responses of a salutogenesis enhanced software tool using psychophysiological measurements"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "using students as experimental subjects in software engineering research a review and discussion of the evidence"

Connected through hyperedge HE-6 (induced by source paper "dpmood exploiting local and periodic typing dynamics for personalized mood prediction"):
  with members:
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-7 (induced by source paper "co morbidity exploration on wearables activity data using unsupervised pre training and multi task learning"):
  with members:
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "sleep quality prediction from wearable data using deep learning"
    - "node2vec scalable feature learning for networks"
    - "conditional random fields as recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"):
  with members:
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "foodporn obesity patterns in culinary interactions"
    - "engaging with massive online courses"
    - "how to ask for a favor a case study on the success of altruistic requests"

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)