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: "understanding types of users on twitter"
  Abstract: People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research work identifies six broad classes of Twitter users, and employs a supervised machine learning approach which uses a comprehensive set of features to classify users into the identified classes. For this purpose, we exploit users' profile and tweeting behavior information. We evaluate our approach by performing 10-fold cross validation using manually annotated 716 different Twitter profiles. High classification accuracy (measured using AUC, and precision, recall) reveals the significance of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "twawler a lightweight twitter crawler"):
  with members:
    - "the darpa twitter bot challenge"
    - "aggregate characterization of user behavior in twitter and analysis of the retweet graph"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-2 (induced by source paper "a review of features for the discrimination of twitter users application to the prediction of offline influence"):
  with members:
    - "temporal multinomial mixture for instance oriented evolutionary clustering"
    - "klout score measuring influence across multiple social networks"
    - "detecting real world influence through twitter"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "identifying the community roles of social capitalists in the twitter network"
    - "artex is another text summarizer"
    - "i wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-3 (induced by source paper "processing social media messages in mass emergency a survey"):
  with members:
    - "where in the world are you geolocation and language identification in twitter"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "tweetcred real time credibility assessment of content on twitter"
    - "engineering crowdsourced stream processing systems"
    - "event evolution tracking from streaming social posts"

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)