human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bootstrapping structure into language alignment based learning"):
  Abstract: refined and abstract meanings largely grow out of more concrete meanings. Bloomfield (1933)#R##N##R##N#This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and Harris's (1951) notion of substitutability . Instances of the framework can be applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled, bracketed version of that corpus. Firstly, the framework aligns all sentences in the corpus in pairs, resulting in a partition of the sentences consisting of parts of the sentences that are equal in both sentences and parts that are unequal. Unequal parts of sen tences can be seen as being substitutable for each other, since substituting one unequal part for the other results in another valid sentence. The unequal parts of the sentences are thus considered to be possible (possibly overlapping) constituents, called hypotheses.#R##N##R##N#Secondly , the selection learning phase considers all hypotheses found by the alignment learning phase and selects the best of these. The hypotheses are selected based on the order in which they were found, or based on a probabilistic function. The framework can be extended with a grammar extraction phase. This extended framework is called parseABL. Instead of returning a structured version of the unstructured input corpus, like the ABL system, this system also returns a stochastic context-free or tree substitution grammar. #R##N##R##N#Different instances of the framework have been tested on the English ATIS corpus, the Dutch OVIS corpus and the Wall Street Journal corpus. One of the interesting results, apart from the encouraging numerical results, is that all instances can (and do) learn recursive structures.

Includes members:
  Member "bootstrapping structure using similarity":
    also connected through other hyperedges:
      (none)
  Member "bootstrapping syntax and recursion using alignment based learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bootstrapping lexical choice via multiple sequence alignment")
  Member "abl alignment based learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised language acquisition theory and practice")
  Member "parsing with the shortest derivation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based shallow parsing")
  Member "an efficient probabilistically sound algorithm for segmentation and word discovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling musical structure with artificial neural networks")
  Member "learning efficient disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is the minimal set of fragments that achieves maximal parse accuracy")
  Member "supervised grammar induction using training data with limited constituent information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "identifying hierarchical structure in sequences a linear time algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")

Question: Please tell me which class the source paper 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)