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 kills in game of thrones using network properties"
  Abstract: TV series such as HBO's most popular show Game of Thrones have seen a high number of dedicated followers, who watch and thoroughly analyze every minute of the show. Largely discussed aspect of the show between viewers seems to be the dramatic murders of the most important characters, the thing that the series is most known for. In our work, we try to predict characters' kills (killer and victim pairs) using data about previous kills by the characters and additional metadata. We construct a network with characters as nodes, where two nodes are linked if one killed the other. Then we use a link prediction framework and evaluate different techniques to predict the next possible kills. Lastly, we construct features from various network properties on a social network of characters, which we use in conjunction with classic data mining techniques. We see that due to the small size of the kills dataset and the somewhat random distribution of kills, we cannot predict much with standard indices. However, we show that we can construct an index which is very customized for the exact network we create for Game of Thrones but might not work on other series. We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield as accurate predictions as we would hope. The best results overall are achieved by using a custom index for link prediction, which is fitting for our type of network and gives us an Area Under the ROC Curve (AUC) of 0.863.

Connected through hyperedge HE-1 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"
    - "a survey of comics research in computer science"

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)