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: "length bias in encoder decoder models and a case for global conditioning"
  Abstract: Encoder-decoder networks are popular for modeling sequences probabilistically in many applications. These models use the power of the Long Short-Term Memory (LSTM) architecture to capture the full dependence among variables, unlike earlier models like CRFs that typically assumed conditional independence among non-adjacent variables. However in practice encoder-decoder models exhibit a bias towards short sequences that surprisingly gets worse with increasing beam size. #R##N#In this paper we show that such phenomenon is due to a discrepancy between the full sequence margin and the per-element margin enforced by the locally conditioned training objective of a encoder-decoder model. The discrepancy more adversely impacts long sequences, explaining the bias towards predicting short sequences. #R##N#For the case where the predicted sequences come from a closed set, we show that a globally conditioned model alleviates the above problems of encoder-decoder models. From a practical point of view, our proposed model also eliminates the need for a beam-search during inference, which reduces to an efficient dot-product based search in a vector-space.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "hierarchical neural story generation"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "calibration of encoder decoder models for neural machine translation"):
  with members:
    - "accurate uncertainties for deep learning using calibrated regression"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "on calibration of modern neural networks"
    - "on fairness and calibration"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "neural machine translation"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "on nmt search errors and model errors cat got your tongue"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "the university of cambridge s machine translation systems for wmt18"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "analyzing neural mt search and model performance"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "diversifying reply suggestions using a matching conditional variational autoencoder"):
  with members:
    - "a hierarchical latent structure for variational conversation modeling"
    - "a collective variational autoencoder for top n recommendation with side information"
    - "generating sentences by editing prototypes"
    - "efficient natural language response suggestion for smart reply"
    - "a conditional variational framework for dialog generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "sequence to sequence learning as beam search optimization"
    - "smart reply automated response suggestion for email"

Connected through hyperedge HE-5 (induced by source paper "better conversations by modeling filtering and optimizing for coherence and diversity"):
  with members:
    - "improving variational encoder decoders in dialogue generation"
    - "toward controlled generation of text"
    - "a knowledge grounded neural conversation model"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to decode for future success"
    - "latent variable dialogue models and their diversity"
    - "steering output style and topic in neural response generation"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "learning to decode for future success"):
  with members:
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "sequence to sequence learning as beam search optimization"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "lstm based conversation models"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "an actor critic algorithm for sequence prediction"
    - "coherent dialogue with attention based language models"

Connected through hyperedge HE-7 (induced by source paper "adversarial evaluation of dialogue models"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "improved techniques for training gans"
    - "smart reply automated response suggestion for email"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "generating sentences from a continuous space"
    - "a diversity promoting objective function for neural conversation models"

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.AI(Artificial Intelligence)