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: "understanding and measuring psychological stress using social media"
  Abstract: A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social media-based measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.

Connected through hyperedge HE-1 (induced by source paper "what twitter profile and posted images reveal about depression and anxiety"):
  with members:
    - "wearing many social hats how different are your different social network personae"
    - "what your facebook profile picture reveals about your personality"
    - "instagram photos reveal predictive markers of depression"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "cooperative phoneotypes exploring phone based behavioral markers of cooperation"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "social media image analysis for public health"

Connected through hyperedge HE-2 (induced by source paper "dreaddit a reddit dataset for stress analysis in social media"):
  with members:
    - "emo2vec learning generalized emotion representation by multi task training"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "studying cultural differences in emoji usage across the east and the west"):
  with members:
    - "what twitter profile and posted images reveal about depression and anxiety"
    - "emojinet an open service and api for emoji sense discovery"
    - "emoji2vec learning emoji representations from their description"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)