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: "epsilon lexicase selection for regression"
  Abstract: Lexicase selection is a parent selection method that considers test cases separately, rather than in aggregate, when performing parent selection. It performs well in discrete error spaces but not on the continuous-valued problems that compose most system identification tasks. In this paper, we develop a new form of lexicase selection for symbolic regression, named e-lexicase selection, that redefines the pass condition for individuals on each test case in a more effective way. We run a series of experiments on real-world and synthetic problems with several treatments of e and quantify how e affects parent selection and model performance. e-lexicase selection is shown to be effective for regression, producing better fit models compared to other techniques such as tournament selection and age-fitness Pareto optimization. We demonstrate that e can be adapted automatically for individual test cases based on the population performance distribution. Our experiments show that e-lexicase selection with automatic e produces the most accurate models across tested problems with negligible computational overhead. We show that behavioral diversity is exceptionally high in lexicase selection treatments, and that e-lexicase selection makes use of more fitness cases when selecting parents than lexicase selection, which helps explain the performance improvement.

Connected through hyperedge HE-1 (induced by source paper "analytic continued fractions for regression a memetic algorithm approach"):
  with members:
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "block building programming for symbolic regression"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-2 (induced by source paper "sgp dt semantic genetic programming based on dynamic targets"):
  with members:
    - "where are we now a large benchmark study of recent symbolic regression methods"

Connected through hyperedge HE-3 (induced by source paper "semantic variation operators for multidimensional genetic programming"):
  with members:
    - "learning concise representations for regression by evolving networks of trees"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "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"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "comparing and combining lexicase selection and novelty search"):
  with members:
    - "lexicase selection of specialists"
    - "illuminating search spaces by mapping elites"
    - "evolution of swarm robotics systems with novelty search"

Connected through hyperedge HE-5 (induced by source paper "batch tournament selection for genetic programming"):
  with members:
    - "self organizing classifiers and niched fitness"
    - "general subpopulation framework and taming the conflict inside populations"

Connected through hyperedge HE-6 (induced by source paper "lexicase selection of specialists"):
  with members:
    - "stepping stones to inductive synthesis of low level looping programs"

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"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "where are we now a large benchmark study of recent symbolic regression methods"):
  with members:
    - "symbolic regression algorithms with built in linear regression"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "openml networked science in machine learning"

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)