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 signals and algorithmic trading of bitcoin"
  Abstract: The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

Connected through hyperedge HE-1 (induced by source paper "identifying and analyzing cryptocurrency manipulations in social media"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "political bots and the manipulation of public opinion in venezuela"

Connected through hyperedge HE-2 (induced by source paper "evolving ab initio trading strategies in heterogeneous environments"):
  with members:
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "an experimental study of cryptocurrency market dynamics"):
  with members:
    - "bots as virtual confederates design and ethics"
    - "botivist calling volunteers to action using online bots"
    - "designing and deploying online field experiments"
    - "using a model of social dynamics to predict popularity of news"

Connected through hyperedge HE-4 (induced by source paper "mutual excitation of cryptocurrency market returns and social media topics"):
  with members:
    - "rapid prototyping of a text mining application for cryptocurrency market intelligence"
    - "nowcasting the bitcoin market with twitter signals"

Connected through hyperedge HE-5 (induced by source paper "the qwerty effect on the web how typing shapes the meaning of words in online human computer interaction"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "sentiment cascades in the 15m movement"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "positive words carry less information than negative words"
    - "happiness is assortative in online social networks"

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)