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: "bootstrapping syntax and recursion using alignment based learning"
  Abstract: This paper introduces a new type of unsupervised learning algorithm, based on the alignment of sentences and Harris's (1951) notion of interchangeability. The algorithm is applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled, bracketed version of the corpus. Firstly, the algorithm aligns all sentences in the corpus in pairs, resulting in a partition of the sentences consisting of parts of the sentences that are similar in both sentences and parts that are dissimilar. This information is used to find (possibly overlapping) constituents. Next, the algorithm selects (non-overlapping) constituents. Several instances of the algorithm are applied to the ATIS corpus (Marcus et al., 1993) and the OVIS (Openbaar Vervoer Informatie Systeem (OVIS) stands for Public Transport Information System.) corpus (Bonnema et al., 1997). Apart from the promising numerical results, the most striking result is that even the simplest algorithm based on alignment learns recursion.

Connected through hyperedge HE-1 (induced by source paper "bootstrapping lexical choice via multiple sequence alignment"):
  with members:
    - "trainable methods for surface natural language generation"
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping structure into language alignment based learning"):
  with members:
    - "bootstrapping structure using similarity"
    - "abl 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)