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: "daily stress recognition from mobile phone data weather conditions and individual traits"
  Abstract: Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous network approach to predict individuals mental health"):
  with members:
    - "the power of dynamic social networks to predict individuals mental health"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "social network structure is predictive of health and wellness"
    - "can we assess mental health through social media and smart devices addressing bias in methodology and evaluation"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-2 (induced by source paper "the power of dynamic social networks to predict individuals mental health"):
  with members:
    - "heterogeneous network approach to predict individuals mental health"
    - "social network structure is predictive of health and wellness"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "dynamic networks reveal key players in aging"
    - "happiness is assortative in online social networks"

Connected through hyperedge HE-3 (induced by source paper "emma an emotion aware wellbeing chatbot"):
  with members:
    - "towards understanding emotional intelligence for behavior change chatbots"

Connected through hyperedge HE-4 (induced by source paper "emma an emotionally intelligent personal assistant for improving wellbeing"):
  with members:
    - "towards understanding emotional intelligence for behavior change chatbots"

Connected through hyperedge HE-5 (induced by source paper "the tyranny of data the bright and dark sides of data driven decision making for social good"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "measuring urban deprivation from user generated content"
    - "smartphones in mental health detecting depressive and manic episodes"
    - "flooding through the lens of mobile phone activity"
    - "once upon a crime towards crime prediction from demographics and mobile data"
    - "money walks a human centric study on the economics of personal mobile data"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-6 (induced by source paper "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"):
  with members:
    - "social contagion theory examining dynamic social networks and human behavior"
    - "twitter mood predicts the stock market"

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)