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: "solving the exponential growth of symbolic regression trees in geometric semantic genetic programming"
  Abstract: Advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression. However, by construction, the geometric semantic crossover operator generates individuals that grow exponentially with the number of generations, resulting in solutions with limited use. This paper presents a new method for individual simplification named GSGP with Reduced trees (GSGP-Red). GSGP-Red works by expanding the functions generated by the geometric semantic operators. The resulting expanded function is guaranteed to be a linear combination that, in a second step, has its repeated structures and respective coefficients aggregated. Experiments in 12 real-world datasets show that it is not only possible to create smaller and completely equivalent individuals in competitive computational time, but also to reduce the number of nodes composing them by 58 orders of magnitude, on average.

Connected through hyperedge HE-1 (induced by source paper "improving model based genetic programming for symbolic regression of small expressions"):
  with members:
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "a probabilistic linear genetic programming with stochastic context free grammar for solving symbolic regression problems"
    - "symbolic regression algorithms with built in linear regression"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "optimization by pairwise linkage detection incremental linkage set and restricted back mixing dsmga ii"
    - "avoiding the bloat with stochastic grammar based genetic programming"

Connected through hyperedge HE-2 (induced by source paper "interaction transformation evolutionary algorithm for symbolic regression"):
  with members:
    - "semantic variation operators for multidimensional genetic programming"
    - "learning concise representations for regression by evolving networks of trees"
    - "a greedy search tree heuristic for symbolic regression"

Connected through hyperedge HE-3 (induced by source paper "a new deterministic technique for symbolic regression"):
  with members:
    - "positional cartesian genetic programming"
    - "julia a fresh approach to numerical computing"

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.NE(Neural and Evolutionary Computing)