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: "semi supervised approach to monitoring clinical depressive symptoms in social media"
  Abstract: With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-2 (induced by source paper "fusing visual textual and connectivity clues for studying mental health"):
  with members:
    - "multimodal classification for analysing social media"
    - "mining online social data for detecting social network mental disorders"
    - "depression and self harm risk assessment in online forums"
    - "instagram photos reveal predictive markers of depression"
    - "xgboost a scalable tree boosting system"
    - "identifying depression on twitter"
    - "social media image analysis for public health"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "inverse boosting pruning trees for depression detection on twitter"):
  with members:
    - "decision stream cultivating deep decision trees"
    - "xgboost a scalable tree boosting system"
    - "identifying depression on twitter"

Connected through hyperedge HE-4 (induced by source paper "safecity understanding diverse forms of sexual harassment personal stories"):
  with members:
    - "automatic detection of cyberbullying in social media text"
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "depression severity estimation from multiple modalities"
    - "interpreting neural networks to improve politeness comprehension"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding neural models in nlp"
    - "a c lstm neural network for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "did you really just have a heart attack towards robust detection of personal health mentions in social media"):
  with members:
    - "are word embedding based features useful for sarcasm detection"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "bag of tricks for efficient text classification"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"

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)