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: "applications of data mining to electronic commerce"
  Abstract: Electronic commerce is emerging as the killer domain for data mining technology. #R##N#The following are five desiderata for success. Seldom are they they all present in one data mining application. #R##N#1. Data with rich descriptions. For example, wide customer records with many potentially useful fields allow data mining algorithms to search beyond obvious correlations. #R##N#2. A large volume of data. The large model spaces corresponding to rich data demand many training instances to build reliable models. #R##N#3. Controlled and reliable data collection. Manual data entry and integration from legacy systems both are notoriously problematic; fully automated collection is considerably better. #R##N#4. The ability to evaluate results. Substantial, demonstrable return on investment can be very convincing. #R##N#5. Ease of integration with existing processes. Even if pilot studies show potential benefit, deploying automated solutions to previously manual processes is rife with pitfalls. Building a system to take advantage of the mined knowledge can be a substantial undertaking. Furthermore, one often must deal with social and political issues involved in the automation of a previously manual business process.

Connected through hyperedge HE-1 (induced by source paper "heavy hitters over interval queries"):
  with members:
    - "a high performance algorithm for identifying frequent items in data streams"
    - "constant time updates in hierarchical heavy hitters"
    - "hierarchical heavy hitters with the space saving algorithm"

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.LG(Machine Learning)