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: "learning to branch"
  Abstract: Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and nonconvex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms recursively partition the search space to find an optimal solution. In order to keep the tree size small, it is crucial to carefully decide, when expanding a tree node, which question (typically variable) to branch on at that node in order to partition the remaining space. Numerous partitioning techniques (e.g., variable selection) have been proposed, but there is no theory describing which technique is optimal. We show how to use machine learning to determine an optimal weighting of any set of partitioning procedures for the instance distribution at hand using samples from the distribution. We provide the first sample complexity guarantees for tree search algorithm configuration. These guarantees bound the number of samples sufficient to ensure that the empirical performance of an algorithm over the samples nearly matches its expected performance on the unknown instance distribution. This thorough theoretical investigation naturally gives rise to our learning algorithm. Via experiments, we show that learning an optimal weighting of partitioning procedures can dramatically reduce tree size, and we prove that this reduction can even be exponential. Through theory and experiments, we show that learning to branch is both practical and hugely beneficial.

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 "learning complexity of simulated annealing"):
  with members:
    - "how much data is sufficient to learn high performing algorithms"
    - "learning to link"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"

Connected through hyperedge HE-3 (induced by source paper "guiding high performance sat solvers with unsat core predictions"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "robustfill neural program learning under noisy i o"
    - "ray a distributed framework for emerging ai applications"
    - "schur number five"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "premise selection for theorem proving by deep graph embedding"

Connected through hyperedge HE-4 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "convolutional neural networks with alternately updated clique"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "densely connected convolutional networks"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"

Connected through hyperedge HE-5 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "learning based compressive subsampling"

Connected through hyperedge HE-6 (induced by source paper "empirical bayes regret minimization"):
  with members:
    - "randomized exploration in generalized linear bandits"
    - "probabilistic model agnostic meta learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a simple neural attentive meta learner"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "collaborative filtering bandits"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-7 (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration"):
  with members:
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "bandits with knapsacks"
    - "bayesian optimization with censored response data"

Connected through hyperedge HE-8 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees"):
  with members:
    - "semi bandit optimization in the dispersed setting"
    - "learning to prune speeding up repeated computations"
    - "procrastinating with confidence near optimal anytime adaptive algorithm configuration"
    - "learning to link"
    - "learning robust search strategies using a bandit based approach"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"

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.AI(Artificial Intelligence)