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: "social media image analysis for public health"
  Abstract: Several projects have shown the feasibility to use textual social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged images from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuable for studying public health. We find that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics. Whereas for most statistics user-provided tags are better features, for predicting excessive drinking machine-generated tags such as "liquid" and "glass" yield better models. This hints at the potential of using machine-generated tags to study substance abuse.

Connected through hyperedge HE-1 (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"
    - "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"

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"
    - "identifying depression on twitter"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "jtav jointly learning social media content representation by fusing textual acoustic and visual features"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "image2song song retrieval via bridging image content and lyric words"
    - "visual and textual sentiment analysis using deep fusion convolutional neural networks"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "enriching word vectors with subword information"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "is saki delicious the food perception gap on instagram and its relation to health"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "towards bottom up analysis of social food"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"
    - "foodporn obesity patterns in culinary interactions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "tracking illicit drug dealing and abuse on instagram using multimodal analysis"):
  with members:
    - "finding street gang members on twitter"
    - "webly supervised learning of convolutional networks"
    - "america tweets china a fine grained analysis of the state and individual characteristics regarding attitudes towards china"
    - "pinterest board recommendation for twitter users"
    - "monitoring potential drug interactions and reactions via network analysis of instagram user timelines"
    - "going deeper with convolutions"

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)