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: "learning best k analogies from data distribution for case based software effort estimation"
  Abstract: Case-Based Reasoning (CBR) has been widely used to generate good software effort estimates. The predictive performance of CBR is a dataset dependent and subject to extremely large space of configuration possibilities. Regardless of the type of adaptation technique, deciding on the optimal number of similar cases to be used before applying CBR is a key challenge. In this paper we propose a new technique based on Bisecting k-medoids clustering algorithm to better understanding the structure of a dataset and discovering the ...

Connected through hyperedge HE-1 (induced by source paper "ensemble of learning project productivity in software effort based on use case points"):
  with members:
    - "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"
    - "fuzzy model tree for early effort estimation"
    - "pareto efficient multi objective optimization for local tuning of analogy based estimation"
    - "a hybrid model for estimating software project effort from use case points"
    - "neural network models for software development effort estimation a comparative study"
    - "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"

Connected through hyperedge HE-2 (induced by source paper "pareto efficient multi objective optimization for local tuning of analogy based estimation"):
  with members:
    - "model tree based adaption strategy for software effort estimation by analogy"

Connected through hyperedge HE-3 (induced by source paper "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"):
  with members:
    - "model tree based adaption strategy for software effort estimation by analogy"

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.SE(Software Engineering)