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 driven neural architecture learning for financial time series forecasting"):
  Abstract: Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult to capture by human-designed models. To tackle the supervised learning task in financial time-series prediction, we propose the application of a recently formulated algorithm that adaptively learns a mapping function, realized by a heterogeneous neural architecture composing of Generalized Operational Perceptron, given a set of labeled data. With a modified objective function, the proposed algorithm can accommodate the frequently observed imbalanced data distribution problem. Experiments on a large-scale Limit Order Book dataset demonstrate that the proposed algorithm outperforms related algorithms, including tensor-based methods which have access to a broader set of input information.

Includes members:
  Member "heterogeneous multilayer generalized operational perceptron":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "subset sampling for progressive neural network learning")
  Member "progressive operational perceptron with memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "subset sampling for progressive neural network learning")
  Member "benchmark dataset for mid price prediction of limit order book data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data normalization for bilinear structures in high frequency financial time series")
  Member "multilinear class specific discriminant analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data normalization for bilinear structures in high frequency financial time series")
  Member "tensor representation in high frequency financial data for price change prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data normalization for bilinear structures in high frequency financial time series")
  Member "progressive learning for systematic design of large neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heterogeneous multilayer generalized operational perceptron")
  Member "neural architecture search with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "predicting the direction of stock market prices using random forest":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "event ticket price prediction with deep neural network on spatial temporal sparse data")

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.LG(Machine Learning)