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: "notimind utilizing responses to smart phone notifications as affective sensors"
  Abstract: Today’s mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users’ emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users’ phones collected thousands of social and system notifications along with affect data collected via self-reported Positive and Negative Affect Schedule tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of post and remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different ”in the wild” classifiers (F-measure 74%–78% within-subject model, 72%–76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral, or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication.

Connected through hyperedge HE-1 (induced by source paper "labelsens enabling real time sensor data labelling at the point of collection on edge computing"):
  with members:
    - "challenges of designing and developing tangible interfaces for mental well being"
    - "comparative study of cnn and rnn for natural language processing"
    - "multi label music genre classification from audio text and images using deep features"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "neural gpus learn algorithms"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "beyond mobile apps a survey of technologies for mental well being"):
  with members:
    - "remote biofeedback sharing opportunities and challenges"
    - "measuring depression symptom severity from spoken language and 3d facial expressions"
    - "cloud based or on device an empirical study of mobile deep inference"
    - "identifying depression on twitter"

Connected through hyperedge HE-3 (induced by source paper "research issues in mining user behavioral rules for context aware intelligent mobile applications"):
  with members:
    - "mining user behavioral rules from smartphone data through association analysis"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "silentphone inferring user unavailability based opportune moments to minimize call interruptions"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"

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.CY(Computers and Society)