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: "task based end to end model learning in stochastic optimization"
  Abstract: With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach for learning probabilistic machine learning models in a manner that directly captures the ultimate task-based objective for which they will be used, within the context of stochastic programming. We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "objective mismatch in model based reinforcement learning"):
  with members:
    - "exploring model based planning with policy networks"
    - "deepmdp learning continuous latent space models for representation learning"
    - "when to trust your model model based policy optimization"
    - "robust reinforcement learning for continuous control with model misspecification"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "differentiable mpc for end to end planning and control"

Connected through hyperedge HE-3 (induced by source paper "decision trees for decision making under the predict then optimize framework"):
  with members:
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "optimal sparse decision trees"
    - "generalization bounds in the predict then optimize framework"
    - "end to end learning and optimization on graphs"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-4 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-5 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"

Connected through hyperedge HE-6 (induced by source paper "end to end learning and optimization on graphs"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "generalization bounds in the predict then optimize framework"
    - "link prediction based on graph neural networks"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "hierarchical graph representation learning with differentiable pooling"
    - "optnet differentiable optimization as a layer in neural networks"
    - "stochastic submodular maximization the case of coverage functions"
    - "neural expectation maximization"

Connected through hyperedge HE-7 (induced by source paper "differentiable convex optimization layers"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "the limited multi label projection layer"
    - "deep declarative networks a new hope"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "neural proximal gradient descent for compressive imaging"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "sparse and constrained attention for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "deepproblog neural probabilistic logic programming"
    - "learning a sat solver from single bit supervision"
    - "deeplogic towards end to end differentiable logical reasoning"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"

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)