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: "abl alignment based learning"
  Abstract: This paper introduces a new type of grammar learning algorithm, inspired by string edit distance (Wagner and Fischer, 1974). The algorithm takes a corpus of flat sentences as input and returns a corpus of labelled, bracketed sentences. The method works on pairs of unstructured sentences that have one or more words in common. When two sentences are divided into parts that are the same in both sentences and parts that are different, this information is used to find parts that are interchangeable. These parts are taken as possible constituents of the same type. After this alignment learning step, the selection learning step selects the most probable constituents from all possible constituents. #R##N#This method was used to bootstrap structure on the ATIS corpus (Marcus et al., 1993) and on the OVIS (Openbaar Vervoer Informatie Systeem (OVIS) stands for Public Transport Information System.) corpus (Bonnema et al., 1997). While the results are encouraging (we obtained up to 89.25 % non-crossing brackets precision), this paper will point out some of the shortcomings of our approach and will suggest possible solutions.

Connected through hyperedge HE-1 (induced by source paper "unsupervised language acquisition theory and practice"):
  with members:
    - "measures of distributional similarity"
    - "induction of first order decision lists results on learning the past tense of english verbs"
    - "learning the past tense of english verbs the symbolic pattern associator vs connectionist models"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping structure into language alignment based learning"):
  with members:
    - "bootstrapping structure using similarity"
    - "bootstrapping syntax and recursion using alignment based learning"
    - "parsing with the shortest derivation"
    - "an efficient probabilistically sound algorithm for segmentation and word discovery"
    - "learning efficient disambiguation"
    - "supervised grammar induction using training data with limited constituent information"
    - "identifying hierarchical structure in sequences a linear time algorithm"
    - "substructure discovery using minimum description length and background knowledge"

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)