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 "robust probabilistic predictive syntactic processing"):
  Abstract: This thesis presents a broad-coverage probabilistic top-down parser, and its application to the problem of language modeling for speech recognition. The parser builds fully connected derivations incrementally, in a single pass from left-to-right across the string. We argue that the parsing approach that we have adopted is well-motivated from a psycholinguistic perspective, as a model that captures probabilistic dependencies between lexical items, as part of the process of building connected syntactic structures. The basic parser and conditional probability models are presented, and empirical results are provided for its parsing accuracy on both newspaper text and spontaneous telephone conversations. Modifications to the probability model are presented that lead to improved performance. A new language model which uses the output of the parser is then defined. Perplexity and word error rate reduction are demonstrated over trigram models, even when the trigram is trained on significantly more data. Interpolation on a word-by-word basis with a trigram model yields additional improvements.

Includes members:
  Member "probabilistic top down parsing and language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable syntax aware language models using knowledge distillation")
  Member "exploiting syntactic structure for natural language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic top down parsing and language modeling")
  Member "compact non left recursive grammars using the selective left corner transform and factoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memoisation purely left recursively and with continuation passing style")
  Member "recognition performance of a structured language model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic top down parsing and language modeling")
  Member "efficient probabilistic top down and left corner parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "in order transition based constituent parsing")
  Member "measuring efficiency in high accuracy broad coverage statistical parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "framework and resources for natural language parser evaluation")
  Member "refinement of a structured language model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic top down parsing and language modeling")
  Member "expoiting syntactic structure for language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "palm a hybrid parser and language model")

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.CL(Computation and Language)