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 "context is key new approaches to neural coherence modeling"):
  Abstract: We formulate coherence modeling as a regression task and propose two novel methods to combine techniques from our setup with pairwise approaches. The first of our methods is a model that we call "first-next," which operates similarly to selection sorting but conditions decision-making on information about already-sorted sentences. The second consists of a technique for adding context to regression-based models by concatenating sentence-level representations with an encoding of its corresponding out-of-order paragraph. This latter model achieves Kendall-tau distance and positional accuracy scores that match or exceed the current state-of-the-art on these metrics. Our results suggest that many of the gains that come from more complex, machine-translation inspired approaches can be achieved with simpler, more efficient models.

Includes members:
  Member "sentence ordering and coherence modeling using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations")
  Member "neural sentence ordering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations")
  Member "neural net models for open domain discourse coherence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating interactive human evaluation with self play for open domain dialog systems")
  Member "catching the drift probabilistic content models with applications to generation and summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic natural language generation using dependency information")
  Member "inferring strategies for sentence ordering in multidocument news summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")

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)