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: "efficient probabilistic top down and left corner parsing"
  Abstract: This paper examines efficient predictive broad-coverage parsing without dynamic programming. In contrast to bottom-up methods, depth-first top-down parsing produces partial parses that are fully connected trees spanning the entire left context, from which any kind of non-local dependency or partial semantic interpretation can in principle be read. We contrast two predictive parsing approaches, top-down and left-corner parsing, and find both to be viable. In addition, we find that enhancement with non-local information not only improves parser accuracy, but also substantially improves the search efficiency.

Connected through hyperedge HE-1 (induced by source paper "in order transition based constituent parsing"):
  with members:
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "shift reduce constituent parsing with neural lookahead features"
    - "bi directional attention with agreement for dependency parsing"
    - "what do recurrent neural network grammars learn about syntax"

Connected through hyperedge HE-2 (induced by source paper "viterbi training in prism"):
  with members:
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "linear tabling strategies and optimizations"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-3 (induced by source paper "infinite probability computation by cyclic explanation graphs"):
  with members:
    - "model checking with probabilistic tabled logic programming"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "xsb extending prolog with tabled logic programming"
    - "linear tabling strategies and optimizations"
    - "on applying or parallelism and tabling to logic programs"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "prefix probabilities from stochastic tree adjoining grammars"

Connected through hyperedge HE-4 (induced by source paper "probabilistic parsing strategies"):
  with members:
    - "expoiting syntactic structure for language modeling"

Connected through hyperedge HE-5 (induced by source paper "probabilistic top down parsing and language modeling"):
  with members:
    - "exploiting syntactic structure for natural language modeling"
    - "exploiting diversity in natural language processing combining parsers"
    - "recognition performance of a structured language model"
    - "estimators for stochastic unification based grammars"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language modeling"

Connected through hyperedge HE-6 (induced by source paper "robust probabilistic predictive syntactic processing"):
  with members:
    - "probabilistic top down parsing and language modeling"
    - "exploiting syntactic structure for natural language modeling"
    - "compact non left recursive grammars using the selective left corner transform and factoring"
    - "recognition performance of a structured language model"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language modeling"

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.CL(Computation and Language)