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: "learning approximate inference networks for structured prediction"
  Abstract: Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among parts of structured outputs. Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them. We replace this use of gradient descent with a neural network trained to approximate structured argmax inference. This "inference network" outputs continuous values that we treat as the output structure. We develop large-margin training criteria for joint training of the structured energy function and inference network. On multi-label classification we report speed-ups of 10-60x compared to (Belanger et al, 2017) while also improving accuracy. For sequence labeling with simple structured energies, our approach performs comparably to exact inference while being much faster at test time. We then demonstrate improved accuracy by augmenting the energy with a "label language model" that scores entire output label sequences, showing it can improve handling of long-distance dependencies in part-of-speech tagging. Finally, we show how inference networks can replace dynamic programming for test-time inference in conditional random fields, suggestive for their general use for fast inference in structured settings.

Connected through hyperedge HE-1 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "design challenges and misconceptions in neural sequence labeling"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-2 (induced by source paper "semantic role labeling with iterative structure refinement"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "second order semantic dependency parsing with end to end neural networks"
    - "capturing argument interaction in semantic role labeling with capsule networks"
    - "benchmarking approximate inference methods for neural structured prediction"
    - "achieving human parity on automatic chinese to english news translation"
    - "polyglot semantic role labeling"
    - "image segmentation by iterative inference from conditional score estimation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-3 (induced by source paper "amortized bethe free energy minimization for learning mrfs"):
  with members:
    - "a tutorial on deep latent variable models of natural language"
    - "inference in probabilistic graphical models by graph neural networks"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-4 (induced by source paper "benchmarking approximate inference methods for neural structured prediction"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "design challenges and misconceptions in neural sequence labeling"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning to embed words in context for syntactic tasks"
    - "classical structured prediction losses for sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "structured output learning with conditional generative flows"):
  with members:
    - "macow masked convolutional generative flow"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "neural autoregressive flows"
    - "deep structured prediction with nonlinear output transformations"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-6 (induced by source paper "graph structured prediction energy networks"):
  with members:
    - "deep structured prediction with nonlinear output transformations"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"

Connected through hyperedge HE-7 (induced by source paper "improving joint training of inference networks and structured prediction energy networks"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "spectral normalization for generative adversarial networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to embed words in context for syntactic tasks"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-8 (induced by source paper "genn predicting correlated drug drug interactions with graph energy neural networks"):
  with members:
    - "gmnn graph markov neural networks"
    - "fast graph representation learning with pytorch geometric"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "neural message passing for quantum chemistry"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "structured prediction energy networks"
    - "conditional random fields as 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)