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 "benchmarking approximate inference methods for neural structured prediction"):
  Abstract: Exact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016). Another approach, proposed recently, is to train a neural network (an "inference network") to perform inference (Tu and Gimpel, 2018). In this paper, we compare these two families of inference methods on three sequence labeling datasets. We choose sequence labeling because it permits us to use exact inference as a benchmark in terms of speed, accuracy, and search error. Across datasets, we demonstrate that inference networks achieve a better speed/accuracy/search error trade-off than gradient descent, while also being faster than exact inference at similar accuracy levels. We find further benefit by combining inference networks and gradient descent, using the former to provide a warm start for the latter.

Includes members:
  Member "variational sequential labelers for semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controllable paraphrase generation with a syntactic exemplar")
  Member "design challenges and misconceptions in neural sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "learning approximate inference networks for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchically refined label attention network for sequence labeling")
  Member "fast and accurate entity recognition with iterated dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "learning to embed words in context for syntactic tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving joint training of inference networks and structured prediction energy networks")
  Member "classical structured prediction losses for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "a continuous relaxation of beam search for end to end training of neural sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable top k operator with optimal transport")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)