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: "mining online social data for detecting social network mental disorders"
  Abstract: An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms towards network intervention"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "link prediction based on graph neural networks"

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"
    - "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"
    - "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 "predicting the flu from instagram"):
  with members:
    - "instagram photos reveal predictive markers of depression"
    - "xgboost a scalable tree boosting system"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "monitoring potential drug interactions and reactions via network analysis of instagram user timelines"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "what the language you tweet says about your occupation"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-5 (induced by source paper "semi supervised approach to monitoring clinical depressive symptoms in social media"):
  with members:
    - "on the challenges of sentiment analysis for dynamic events"

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)