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 "task based learning via task oriented prediction network"):
  Abstract: Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning metrics, such as mean squared error, mean absolute error, and cross-entropy loss, but are critical for decision making. A key challenge for direct integration of more meaningful domain and task-based evaluation criteria into an end-to-end gradient-based training process is the fact that often such performance objectives are not necessarily differentiable and may even require additional decision-making optimization processing. We propose Task-Oriented Prediction Network (TOPNet), an end-to-end learning scheme that automatically integrates task-based evaluation criteria into the learning process via a task-oriented estimator and directly learns a model with respect to the task-based goal. A major benefit of the proposed TOPNet learning scheme lies in its capability of automatically integrating non-differentiable evaluation criteria. This makes it particularly suitable for diversified and customized task-based evaluation criteria in real-world prediction tasks. We validate the performance of TOPNet on two real-world financial prediction tasks, revenue surprise forecasting and credit risk modeling. The experimental results on multiple real-world data sets demonstrate that TOPNet significantly outperforms both traditional modeling with standard losses and modeling with differentiable (relaxed) surrogate losses.

Includes members:
  Member "decision focused learning of adversary behavior in security games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mipaal mixed integer program as a layer")
  Member "end to end learning and optimization on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "melding the data decisions pipeline decision focused learning for combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "task based end to end model learning in stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)