human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data normalization for bilinear structures in high frequency financial time series"):
  Abstract: Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 millions order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.

Includes members:
  Member "multilinear compressive learning with prior knowledge":
    also connected through other hyperedges:
      (none)
  Member "multilinear compressive learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "improving efficiency in convolutional neural networks with multilinear filters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "heterogeneous multilayer generalized operational perceptron")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "benchmark dataset for mid price prediction of limit order book data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data driven neural architecture learning for financial time series forecasting")
  Member "multilinear class specific discriminant analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilinear compressive learning")
  Member "tensor representation in high frequency financial data for price change prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilinear compressive learning")
  Member "temporal attention augmented bilinear network for financial time series data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting")

Question: Please tell me which class the source paper 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.CE(Computational Engineering, Finance, and Science)