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: "identifying depression on twitter"
  Abstract: Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of prior social media postings, we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage several statistical classifiers to provide estimates to the risk of depression. Our work posits a new methodology for constructing our classifier by treating social as a text-classification problem, rather than a behavioral one on social media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy rate in classification, with a precision score of .86. We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.

Connected through hyperedge HE-1 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "detection of the prodromal phase of bipolar disorder from psychological and phonological aspects in social media"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

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"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "instagram photos reveal predictive markers of depression"
    - "xgboost a scalable tree boosting system"
    - "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 "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"
    - "notimind utilizing responses to smart phone notifications as affective sensors"
    - "cloud based or on device an empirical study of mobile deep inference"

Connected through hyperedge HE-4 (induced by source paper "inverse boosting pruning trees for depression detection on twitter"):
  with members:
    - "decision stream cultivating deep decision trees"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "anxious depression prediction in real time social data"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "feature studies to inform the classification of depressive symptoms from twitter data for population health"
    - "instagram photos reveal predictive markers of depression"

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.SI(Social and Information Networks)