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: "instagram photos reveal predictive markers of depression"
  Abstract: Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners' average diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Photos posted by depressed individuals were more likely to be bluer, grayer, and darker. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These findings suggest new avenues for early screening and detection of mental illness.

Connected through hyperedge HE-1 (induced by source paper "see and read detecting depression symptoms in higher education students using multimodal social media data"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "exploring the limits of weakly supervised pretraining"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "what twitter profile and posted images reveal about depression and anxiety"):
  with members:
    - "understanding and measuring psychological stress using social media"
    - "wearing many social hats how different are your different social network personae"
    - "what your facebook profile picture reveals about your personality"
    - "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-4 (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"
    - "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-5 (induced by source paper "automating dynamic consent decisions for the processing of social media data in health research"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "some hci priorities for gdpr compliant machine learning"
    - "contextual consent ethical mining of social media for health research"
    - "early stage influenza detection from twitter"

Connected through hyperedge HE-6 (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"
    - "identifying depression on twitter"

Connected through hyperedge HE-7 (induced by source paper "vicsom visual clues from social media for psychological assessment"):
  with members:
    - "multi label learning with global and local label correlation"
    - "efficient estimation of word representations in vector space"
    - "multi instance multi label learning"

Connected through hyperedge HE-8 (induced by source paper "towards understanding political interactions on instagram"):
  with members:
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"

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)