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: "a tree based decoder for neural machine translation"
  Abstract: Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU.

Connected through hyperedge HE-1 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-2 (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"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-3 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "insertion based decoding with automatically inferred generation order"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "sequence generation from both sides to the middle"
    - "non monotonic sequential text generation"
    - "text infilling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"

Connected through hyperedge HE-5 (induced by source paper "latent part of speech sequences for neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "discrete structural planning for neural machine translation"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning to parse and translate improves neural machine translation"
    - "syntax directed attention for neural machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "learning latent trees with stochastic perturbations and differentiable dynamic programming"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "towards dynamic computation graphs via sparse latent structure"
    - "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-7 (induced by source paper "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "syntactic scaffolds for semantic structures"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "language learning using speech to image retrieval"):
  with members:
    - "learning semantic sentence representations from visually grounded language without lexical knowledge"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "snapshot ensembles train 1 get m for free"
    - "representations of language in a model of visually grounded speech signal"
    - "skip thought vectors"

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)