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: "backpropagating through structured argmax using a spigot"
  Abstract: We introduce the structured projection of intermediate gradients optimization technique (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e.g., parsing) in intermediate layers. SPIGOT requires no marginal inference, unlike structured attention networks (Kim et al., 2017) and some reinforcement learning-inspired solutions (Yogatama et al., 2017). Like so-called straight-through estimators (Hinton, 2012), SPIGOT defines gradient-like quantities associated with intermediate nondifferentiable operations, allowing backpropagation before and after them; SPIGOT's proxy aims to ensure that, after a parameter update, the intermediate structure will remain well-formed. #R##N#We experiment on two structured NLP pipelines: syntactic-then-semantic dependency parsing, and semantic parsing followed by sentiment classification. We show that training with SPIGOT leads to a larger improvement on the downstream task than a modularly-trained pipeline, the straight-through estimator, and structured attention, reaching a new state of the art on semantic dependency parsing.

Connected through hyperedge HE-1 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks"):
  with members:
    - "augmenting neural networks with first order logic"
    - "neural theorem provers do not learn rules without exploration"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "semantically regularized logic graph embeddings"
    - "pytorch an imperative style high performance deep learning library"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-3 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"

Connected through hyperedge HE-4 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"
    - "contextual explanation networks"

Connected through hyperedge HE-5 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "transformer xl attentive language models beyond a fixed length context"
    - "direct output connection for a high rank language model"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "augmenting neural networks with first order logic"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "towards semi supervised learning for deep semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-7 (induced by source paper "learning latent trees with stochastic perturbations and differentiable dynamic programming"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "towards dynamic computation graphs via sparse latent structure"
    - "a tree based decoder for neural machine translation"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "grammar induction with neural language models an unusual replication"
    - "structured attention networks"
    - "learning structured text representations"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "interpretable neural predictions with differentiable binary variables"):
  with members:
    - "towards dynamic computation graphs via sparse latent structure"
    - "deep contextualized word representations"
    - "explaining explanations an overview of interpretability of machine learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "a decomposable attention model for natural language inference"

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)