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: "differentiable scheduled sampling for credit assignment"
  Abstract: We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding for sequence-to-sequence (seq2seq) models. By incorporating this approximation into the scheduled sampling training procedure (Bengio et al., 2015)--a well-known technique for correcting exposure bias--we introduce a new training objective that is continuous and differentiable everywhere and that can provide informative gradients near points where previous decoding decisions change their value. In addition, by using a related approximation, we demonstrate a similar approach to sampled-based training. Finally, we show that our approach outperforms cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-2 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hint based training for non autoregressive machine translation"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "fast structured decoding for sequence models"
    - "pnat non autoregressive transformer by position learning"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"):
  with members:
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "selective encoding for abstractive sentence summarization"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "memory augmented neural networks with wormhole connections"
    - "controllable abstractive summarization"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "differentiable sampling with flexible reference word order for neural machine translation"):
  with members:
    - "a study of reinforcement learning for neural machine translation"
    - "bag of words as target for neural machine translation"
    - "bi directional differentiable input reconstruction for low resource neural machine translation"
    - "deep reinforcement learning that matters"
    - "sockeye a toolkit for neural machine translation"
    - "searnn training rnns with global local losses"
    - "learning to compose task specific tree structures"
    - "reward augmented maximum likelihood for neural structured prediction"

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:
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "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 "error correcting neural sequence prediction"):
  with members:
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural machine translation via binary code prediction"
    - "compressing word embeddings via deep compositional code learning"
    - "efficient softmax approximation for gpus"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "strategies for training large vocabulary neural language models"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "scheduled sampling for transformers"):
  with members:
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "sequence to sequence learning as beam search optimization"
    - "an actor critic algorithm for sequence prediction"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "neural machine translation of rare words with subword units"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "making classical machine learning pipelines differentiable a neural translation approach"):
  with members:
    - "machine learning at microsoft with ml net"
    - "achieving human parity on automatic chinese to english news translation"
    - "deep neural decision trees"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)