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: "melding the data decisions pipeline decision focused learning for combinatorial optimization"
  Abstract: Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision. However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible. Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely). #R##N#We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce high-quality decisions. Technically, our contribution is a means of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are typically trained via gradient descent. The main idea is to use a continuous relaxation of the discrete problem to propagate gradients through the optimization procedure. We instantiate this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decision-focused learning often leads to improved optimization performance compared to traditional methods. We find that standard measures of accuracy are not a reliable proxy for a predictive model's utility in optimization, and our method's ability to specify the true goal as the model's training objective yields substantial dividends across a range of decision problems.

Connected through hyperedge HE-1 (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"
    - "task based end to end model learning in stochastic optimization"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-2 (induced by source paper "ai for social impact learning and planning in the data to deployment pipeline"):
  with members:
    - "group fairness in influence maximization"
    - "learning to prescribe interventions for tuberculosis patients using digital adherence data"
    - "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"

Connected through hyperedge HE-3 (induced by source paper "a lagrangian dual framework for deep neural networks with constraints"):
  with members:
    - "optnet differentiable optimization as a layer in neural networks"

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 "deep reasoning networks thinking fast and slow"):
  with members:
    - "pdp a general neural framework for learning constraint satisfaction solvers"
    - "a pca like autoencoder"
    - "deep generative models with learnable knowledge constraints"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "recurrent relational networks for complex relational reasoning"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "end to end learning for the deep multivariate probit model"

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"
    - "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"
    - "task based end to end model learning in stochastic optimization"

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"
    - "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"
    - "what game are we playing end to end learning in normal and extensive form games"

Connected through hyperedge HE-8 (induced by source paper "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"):
  with members:
    - "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"
    - "recurrent relational networks"

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)