human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "what twitter profile and posted images reveal about depression and anxiety"):
  Abstract: Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.

Includes members:
  Member "understanding and measuring psychological stress using social media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dreaddit a reddit dataset for stress analysis in social media")
  Member "wearing many social hats how different are your different social network personae":
    also connected through other hyperedges:
      (none)
  Member "what your facebook profile picture reveals about your personality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measuring similarity between brands using followers post in social media")
  Member "instagram photos reveal predictive markers of depression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "see and read detecting depression symptoms in higher education students using multimodal social media data")
  Member "human pose estimation from depth images via inference embedded multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image")
  Member "greysanatomy vs yankees demographics and hashtag use on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "whose tweets are surveilled for the police an audit of social media monitoring tool via log files")
  Member "cooperative phoneotypes exploring phone based behavioral markers of cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding and measuring psychological stress using social media")
  Member "photo aesthetics ranking network with attributes and content adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aesthetic quality assessment for group photograph")

Question: Please tell me which class the source paper 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.HC(Human-Computer Interaction)