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: "where are we now a large benchmark study of recent symbolic regression methods"
  Abstract: In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community.

Connected through hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming"):
  with members:
    - "evolving boolean functions with conjunctions and disjunctions via genetic programming"
    - "on inversely proportional hypermutations with mutation potential"
    - "bounding bloat in genetic programming"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "computational complexity analysis of multi objective genetic programming"
    - "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics"

Connected through hyperedge HE-2 (induced by source paper "analytic continued fractions for regression a memetic algorithm approach"):
  with members:
    - "block building programming for symbolic regression"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "xgboost a scalable tree boosting system"
    - "epsilon lexicase selection for regression"
    - "adam a method for stochastic optimization"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-3 (induced by source paper "sgp dt semantic genetic programming based on dynamic targets"):
  with members:
    - "epsilon lexicase selection for regression"

Connected through hyperedge HE-4 (induced by source paper "improving model based genetic programming for symbolic regression of small expressions"):
  with members:
    - "solving the exponential growth of symbolic regression trees in geometric semantic genetic programming"
    - "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-5 (induced by source paper "semantic variation operators for multidimensional genetic programming"):
  with members:
    - "learning concise representations for regression by evolving networks of trees"
    - "ensemble representation learning an analysis of fitness and survival for wrapper based genetic programming methods"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "xgboost a scalable tree boosting system"
    - "epsilon lexicase selection for regression"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "a novel continuous representation of genetic programmings using recurrent neural networks for symbolic regression"):
  with members:
    - "text generation based on generative adversarial nets with latent variable"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "generating sentences from a continuous space"
    - "gene expression programming a new adaptive algorithm for solving problems"

Connected through hyperedge HE-7 (induced by source paper "learning concise representations for regression by evolving networks of trees"):
  with members:
    - "a two step disentanglement method"
    - "efficient neural architecture search via parameter sharing"
    - "image to image translation for cross domain disentanglement"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "population based training of neural networks"

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)