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 networks that matter twitter under the microscope"
  Abstract: Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the declared set of friends and followers.

Connected through hyperedge HE-1 (induced by source paper "consistent recovery threshold of hidden nearest neighbor graphs"):
  with members:
    - "hidden hamiltonian cycle recovery via linear programming"
    - "detecting strong ties using network motifs"

Connected through hyperedge HE-2 (induced by source paper "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"):
  with members:
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "a survey of location inference techniques on twitter"
    - "confounds and consequences in geotagged twitter data"
    - "computational sociolinguistics a survey"
    - "statistically significant detection of linguistic change"
    - "where in the world are you geolocation and language identification in twitter"
    - "diffusion of lexical change in social media"

Connected through hyperedge HE-3 (induced by source paper "predicting twitter user socioeconomic attributes with network and language information"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "probabilistic inference of twitter users age based on what they follow"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-4 (induced by source paper "stylistic variation in social media part of speech tagging"):
  with members:
    - "toward socially infused information extraction embedding authors mentions and entities"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-5 (induced by source paper "directed random geometric graphs"):
  with members:
    - "degree relations of triangles in real world networks and graph models"

Connected through hyperedge HE-6 (induced by source paper "who will share my image predicting the content diffusion path in online social networks"):
  with members:
    - "predicting the popularity of github repositories"
    - "node2vec scalable feature learning for networks"
    - "understanding image virality"
    - "densecap fully convolutional localization networks for dense captioning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-7 (induced by source paper "learning user representations in online social networks using temporal dynamics of information diffusion"):
  with members:
    - "on the role of conductance geography and topology in predicting hashtag virality"
    - "line large scale information network embedding"
    - "the bursty dynamics of the twitter information network"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "emo love and god making sense of urban dictionary a crowd sourced online dictionary"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "learning to explain non standard english words and phrases"
    - "diffusion of lexical change in social media"
    - "novelty and collective attention"

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.CY(Computers and Society)