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: "mining half a billion topical experts across multiple social networks"
  Abstract: Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks—Twitter, Facebook, Google+ and LinkedIn—along with the Wikipedia graph and Internet webpage text and metadata, to rank topical experts across the global population of users. We perform an in-depth analysis of 37 features derived from various data sources such as message text, user lists, webpages, social graphs and Wikipedia. This large-scale study includes more than 12 billion messages over a 90-day sliding window and 58 billion social graph edges. Comparison reveals that features derived from Twitter Lists, Wikipedia, Internet webpages and Twitter Followers are especially good indicators of expertise. We train an expertise ranking model using these features on a large ground-truth dataset containing almost 90,000 labels. This model is applied within a production system that ranks over 650 million experts in more than 9000 topical domains on a daily basis. We provide results and examples on the effectiveness of our expert ranking system, along with empirical validation. Finally, we make the topical expertise data available through open REST APIs for wider use.

Connected through hyperedge HE-1 (induced by source paper "dawt densely annotated wikipedia texts across multiple languages"):
  with members:
    - "high throughput and language agnostic entity disambiguation and linking on user generated data"
    - "global entity ranking across multiple languages"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"
    - "mining meaning from wikipedia"

Connected through hyperedge HE-2 (induced by source paper "lithium nlp a system for rich information extraction from noisy user generated text on social media"):
  with members:
    - "dawt densely annotated wikipedia texts across multiple languages"
    - "high throughput and language agnostic entity disambiguation and linking on user generated data"
    - "global entity ranking across multiple languages"
    - "identifying actionable messages on social media"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-3 (induced by source paper "analyzing users sentiment towards popular consumer industries and brands on twitter"):
  with members:
    - "klout topics for modeling interests and expertise of users across social networks"
    - "recent trends in deep learning based natural language processing"
    - "lithium nlp a system for rich information extraction from noisy user generated text on social media"
    - "actionable and political text classification using word embeddings and lstm"
    - "identifying actionable messages on social media"

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.IR(Information Retrieval)