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: "the nlp engine a universal turing machine for nlp"
  Abstract: It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a universal framework akin to a Turning Machine that attempts to fit (most) NLP tasks into one paradigm. We calculate the complexities of various NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as part of speech tagging to `complex' ones such as machine translation. This paper provides a first, though far from perfect, attempt to quantify NLP tasks under a uniform paradigm. We point out current deficiencies and suggest some avenues for fruitful research.

Connected through hyperedge HE-1 (induced by source paper "neural associative memory for dual sequence modeling"):
  with members:
    - "associative long short term memory"
    - "long short term memory networks for machine reading"
    - "end to end memory networks"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "learning to transduce with unbounded memory"
    - "learning natural language inference with lstm"
    - "a large annotated corpus for learning natural language inference"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical neural autoencoder for paragraphs and documents"):
  with members:
    - "show attend and tell neural image caption generation with visual attention"
    - "overcoming the curse of sentence length for neural machine translation using automatic segmentation"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine translation"
    - "generating sequences with recurrent neural networks"

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)