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: "block building programming for symbolic regression"
  Abstract: Symbolic regression that aims to detect underlying data-driven model, has become increasingly important for industrial data analysis when the experimental model structure is unknown or wrong, or the concerned system has changed. For most of the existing algorithms for symbolic regression, such as genetic programming, the convergence speed might be too slow for large scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty make symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP), in this paper. BBP divides the original target model into several simple models, and then optimizes them sequentially. Under such circumstance, BBP can make large reductions to the search space. The partition of separability is based on a designed method, block and factor detection. Using two different optimization engines, experiments on a set of symbolic regression problems with separability are conducted. Numerical results show that BBP has a good capability of structure and coefficient optimization and a better computational efficiency.

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"
    - "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-2 (induced by source paper "fast modeling methods for complex system with separable features"):
  with members:
    - "a divide and conquer method for symbolic regression"
    - "elite bases regression a real time algorithm for symbolic regression"

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)