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: "aslib a benchmark library for algorithm selection"
  Abstract: The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

Connected through hyperedge HE-1 (induced by source paper "extreme algorithm selection with dyadic feature representation"):
  with members:
    - "automated algorithm selection survey and perspectives"
    - "openml networked science in machine learning"
    - "a theoretical analysis of ndcg type ranking measures"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "restart strategy selection using machine learning techniques"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-2 (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"
    - "learning to branch"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "fast counting with tensor networks"):
  with members:
    - "solving and sampling with many solutions satisfiability and other hard problems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "implementing efficient all solutions sat solvers"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "recent advances in graph partitioning"
    - "satzilla portfolio based algorithm selection for sat"
    - "a knowledge compilation map"

Connected through hyperedge HE-4 (induced by source paper "transfer learning for algorithm recommendation"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "transfer learning with neural automl"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "algorithm selection for image quality assessment"):
  with members:
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "taking human out of learning applications a survey on automated machine learning"):
  with members:
    - "meta learning a survey"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "efficient progressive neural architecture search"
    - "darts differentiable architecture search"
    - "transfer learning with neural automl"

Connected through hyperedge HE-7 (induced by source paper "machine learning for combinatorial optimization a methodological tour d horizon"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "predicting tactical solutions to operational planning problems under imperfect information"
    - "learning to optimize neural nets"
    - "automated treatment planning in radiation therapy using generative adversarial networks"
    - "learning a sat solver from single bit supervision"
    - "deterministic implementations for reproducibility in deep reinforcement learning"
    - "generative adversarial networks an overview"
    - "learning permutations with sinkhorn policy gradient"

Connected through hyperedge HE-8 (induced by source paper "the algorithm selection competitions 2015 and 2017"):
  with members:
    - "openml networked science in machine learning"
    - "a multi engine approach to answer set programming"
    - "sunny a lazy portfolio approach for constraint solving"
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "proteus a hierarchical portfolio of solvers and transformations"
    - "a bayesian approach to tackling hard computational problems"
    - "llama leveraging learning to automatically manage algorithms"
    - "algorithm runtime prediction methods evaluation"

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)