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 abnormal returns from news using text classification"
  Abstract: We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.

Connected through hyperedge HE-1 (induced by source paper "event representation learning enhanced with external commonsense knowledge"):
  with members:
    - "story ending prediction by transferable bert"
    - "deep contextualized word representations"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "event representations with tensor based compositions"
    - "deep biaffine attention for neural dependency parsing"
    - "a study of entanglement in a categorical framework of natural language"
    - "learning semantic script knowledge with event embeddings"

Connected through hyperedge HE-2 (induced by source paper "a tensor based sub mode coordinate algorithm for stock prediction"):
  with members:
    - "improving stock market prediction via heterogeneous information fusion"

Connected through hyperedge HE-3 (induced by source paper "social network based short term stock trading system"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "improving stock market prediction via heterogeneous information fusion"):
  with members:
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "coupled matrix factorization within non iid context"
    - "distributed representations of words and phrases and their compositionality"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "exploiting investors social network for stock prediction in china s market"):
  with members:
    - "investigation into the effectiveness of long short term memory networks for stock price prediction"
    - "twitter mood predicts the stock market"

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.LG(Machine Learning)