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: "task loss estimation for sequence prediction"
  Abstract: Often, the performance on a supervised machine learning task is evaluated with a emph{task loss} function that cannot be optimized directly. Examples of such loss functions include the classification error, the edit distance and the BLEU score. A common workaround for this problem is to instead optimize a emph{surrogate loss} function, such as for instance cross-entropy or hinge loss. In order for this remedy to be effective, it is important to ensure that minimization of the surrogate loss results in minimization of the task loss, a condition that we call emph{consistency with the task loss}. In this work, we propose another method for deriving differentiable surrogate losses that provably meet this requirement. We focus on the broad class of models that define a score for every input-output pair. Our idea is that this score can be interpreted as an estimate of the task loss, and that the estimation error may be used as a consistent surrogate loss. A distinct feature of such an approach is that it defines the desirable value of the score for every input-output pair. We use this property to design specialized surrogate losses for Encoder-Decoder models often used for sequence prediction tasks. In our experiment, we benchmark on the task of speech recognition. Using a new surrogate loss instead of cross-entropy to train an Encoder-Decoder speech recognizer brings a significant ~13% relative improvement in terms of Character Error Rate (CER) in the case when no extra corpora are used for language modeling.

Connected through hyperedge HE-1 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-2 (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"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "cold fusion training seq2seq models together with language models"

Connected through hyperedge HE-3 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "twin networks matching the future for sequence generation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "learning to decode for future success"
    - "regularizing and optimizing lstm language models"
    - "memory augmented neural networks with wormhole connections"
    - "reading wikipedia to answer open domain questions"
    - "neural autoregressive distribution estimation"
    - "the predictron end to end learning and planning"

Connected through hyperedge HE-5 (induced by source paper "reward augmented maximum likelihood for neural structured prediction"):
  with members:
    - "sequence to sequence learning as beam search optimization"
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"
    - "listen attend and spell"
    - "learning dynamic feature selection for fast sequential prediction"
    - "effective approaches to attention based neural machine translation"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "very deep convolutional networks for end to end speech recognition"):
  with members:
    - "advances in very deep convolutional neural networks for lvcsr"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "grid long short term memory"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "noisy parallel approximate decoding for conditional recurrent language model"):
  with members:
    - "mutual information and diverse decoding improve neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "a recurrent latent variable model for sequential data"
    - "neural machine translation of rare words with subword units"
    - "sequence level training with recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "describing multimedia content using attention based encoder decoder networks"
    - "adam a method for stochastic optimization"

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)