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: "you are what apps you use demographic prediction based on user s apps"
  Abstract: Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 % accuracy), whereas the hardest to predict is income (60.3 % accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.

Connected through hyperedge HE-1 (induced by source paper "predicting multiple demographic attributes with task specific embedding transformation and attention network"):
  with members:
    - "fusing multifaceted transaction data for user modeling and demographic prediction"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "cross stitch networks for multi task learning"
    - "hierarchical question image co attention for visual question answering"
    - "multi task sequence to sequence learning"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "predicting demographics moral foundations and human values from digital behaviours"):
  with members:
    - "studying migrant assimilation through facebook interests"
    - "acquiring background knowledge to improve moral value prediction"
    - "how well can machine learning predict demographics of social media users"
    - "stereotyping and bias in the flickr30k dataset"
    - "a survey on measuring indirect discrimination in machine learning"
    - "using deep learning to predict demographics from mobile phone metadata"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "estimating group properties in online social networks with a classifier"):
  with members:
    - "white man and highly followed gender and race inequalities in twitter"
    - "sampling from social networks with attributes"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-4 (induced by source paper "prepaid or postpaid that is the question novel methods of subscription type prediction in mobile phone services"):
  with members:
    - "community detection in networks with node attributes"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"

Connected through hyperedge HE-5 (induced by source paper "greysanatomy vs yankees demographics and hashtag use on twitter"):
  with members:
    - "characterizing the demographics behind the blacklivesmatter movement"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "dynamical classes of collective attention in twitter"

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)