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: "automated algorithm selection survey and perspectives"
  Abstract: It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. #R##N#This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

Connected through hyperedge HE-1 (induced by source paper "online preselection with context information under the plackett luce model"):
  with members:
    - "preselection bandits under the plackett luce model"
    - "regret minimisation in multinomial logit bandits"
    - "multinomial logit bandit with linear utility functions"
    - "preference based online learning with dueling bandits a survey"
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"

Connected through hyperedge HE-2 (induced by source paper "extreme algorithm selection with dyadic feature representation"):
  with members:
    - "aslib a benchmark library for algorithm selection"
    - "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-3 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler"):
  with members:
    - "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"
    - "interpolating local and global search by controlling the variance of standard bit mutation"
    - "hyper parameter tuning for the 1 λ λ ga"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "complexity theory for discrete black box optimization heuristics"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"

Connected through hyperedge HE-4 (induced by source paper "sequential vs integrated algorithm selection and configuration a case study for the modular cma es"):
  with members:
    - "online selection of cma es variants"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "pitfalls and best practices in algorithm configuration"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "evolving the structure of evolution strategies"
    - "coco performance assessment"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-5 (induced by source paper "preselection bandits under the plackett luce model"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "regret minimisation in multinomial logit bandits"
    - "preference based online learning with dueling bandits a survey"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-6 (induced by source paper "encoding selection for solving hamiltonian cycle problems with asp"):
  with members:
    - "lpopt a rule optimization tool for answer set programming"
    - "a multi engine approach to answer set programming"
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "satzilla portfolio based algorithm selection for sat"
    - "the dlv system for knowledge representation and reasoning"

Connected through hyperedge HE-7 (induced by source paper "instruction level design of local optimisers using push gp"):
  with members:
    - "mitigating metaphors a comprehensible guide to recent nature inspired algorithms"
    - "learning to optimize neural nets"
    - "learned optimizers that scale and generalize"
    - "evolving the structure of evolution strategies"

Connected through hyperedge HE-8 (induced by source paper "online selection of cma es variants"):
  with members:
    - "evolving the structure of evolution strategies"
    - "coco performance assessment"
    - "cma es with two point step size adaptation"

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)