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 continuous relaxation of beam search for end to end training of neural sequence models"
  Abstract: Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined, this "direct loss" objective is itself discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.

Connected through hyperedge HE-1 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-2 (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"
    - "learning approximate inference networks for structured prediction"
    - "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"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "understanding back translation at scale"
    - "neural machine translation with gumbel greedy decoding"
    - "attention is all you need"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-4 (induced by source paper "a fully differentiable beam search decoder"):
  with members:
    - "fully convolutional speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "cold fusion training seq2seq models together with language models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-5 (induced by source paper "an empirical investigation of global and local normalization for recurrent neural sequence models using a continuous relaxation to beam search"):
  with members:
    - "differentiable scheduled sampling for credit assignment"
    - "sequence to sequence learning as beam search optimization"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "input convex neural networks"
    - "a neural attention model for abstractive sentence summarization"
    - "structured prediction energy networks"

Connected through hyperedge HE-6 (induced by source paper "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "deep contextualized word representations"
    - "structured attention networks"
    - "learning structured text representations"
    - "neural probabilistic model for non projective mst parsing"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "do latent tree learning models identify meaningful structure in sentences"

Connected through hyperedge HE-7 (induced by source paper "value based search in execution space for mapping instructions to programs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "representation learning for grounded spatial reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-8 (induced by source paper "learning approximate inference networks for structured prediction"):
  with members:
    - "calibrating energy based generative adversarial networks"
    - "sequence level knowledge distillation"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "input convex neural networks"
    - "structured prediction energy networks"
    - "a neural algorithm of artistic style"

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.LG(Machine Learning)