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: "predicting twitter user socioeconomic attributes with network and language information"
  Abstract: Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science. Automated inference of such characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. While previous work has shown that language features can reliably predict socioeconomic attributes on Twitter, employing information coming from users' social networks has not yet been explored for such complex user characteristics. In this paper, we describe a method for predicting the occupational class and the income of Twitter users given information extracted from their extended networks by learning a low-dimensional vector representation of users, i.e. graph embeddings. We use this representation to train predictive models for occupational class and income. Results on two publicly available datasets show that our method consistently outperforms the state-of-the-art methods in both tasks. We also obtain further significant improvements when we combine graph embeddings with textual features, demonstrating that social network and language information are complementary.

Connected through hyperedge HE-1 (induced by source paper "urban2vec incorporating street view imagery and pois for multi modal urban neighborhood embedding"):
  with members:
    - "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "node2vec scalable feature learning for networks"
    - "learning deep structure preserving image text embeddings"
    - "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"
    - "rethinking the inception architecture for computer vision"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "location occupation and semantics based socioeconomic status inference on twitter"):
  with members:
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "what the language you tweet says about your occupation"
    - "xgboost a scalable tree boosting system"
    - "detecting the age of twitter users"
    - "diffusion of lexical change in social media"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "nowcasting the stance of social media users in a sudden vote the case of the greek referendum"):
  with members:
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "s i o no qu e penses catalonian independence and linguistic identity on social media"
    - "stance classification of social media users in independence movements"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "i have a feeling trump will win forecasting winners and losers from user predictions on twitter"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)