human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "improving model based genetic programming for symbolic regression of small expressions"):
  Abstract: The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

Includes members:
  Member "where are we now a large benchmark study of recent symbolic regression methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming")
  Member "solving the exponential growth of symbolic regression trees in geometric semantic genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interaction transformation evolutionary algorithm for symbolic regression")
  Member "a probabilistic linear genetic programming with stochastic context free grammar for solving symbolic regression problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysing symbolic regression benchmarks under a meta learning approach")
  Member "symbolic regression algorithms with built in linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "where are we now a large benchmark study of recent symbolic regression methods")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "optimization by pairwise linkage detection incremental linkage set and restricted back mixing dsmga ii":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep optimisation solving combinatorial optimisation problems using deep neural networks")
  Member "avoiding the bloat with stochastic grammar based genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "consistent feature construction with constrained genetic programming for experimental physics")

Question: Please tell me which class the source paper 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.NE(Neural and Evolutionary Computing)