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: "learning hard alignments with variational inference"
  Abstract: There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task.

Connected through hyperedge HE-1 (induced by source paper "select and attend towards controllable content selection in text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "towards content transfer through grounded text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "bottom up abstractive summarization"
    - "show control and tell a framework for generating controllable and grounded captions"

Connected through hyperedge HE-2 (induced by source paper "segma semi supervised gaussian mixture auto encoder"):
  with members:
    - "content and linguistic biases in the peer review process of artificial intelligence conferences"
    - "variational information bottleneck for unsupervised clustering deep gaussian mixture embedding"
    - "interpolation in generative models"
    - "cramer wold autoencoder"
    - "generating sentences by editing prototypes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "sliced wasserstein distance for learning gaussian mixture models"

Connected through hyperedge HE-3 (induced by source paper "monotonic multihead attention"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "direct optimization through arg max for discrete variational auto encoder"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "toward controlled generation of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-5 (induced by source paper "an online attention based model for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "online segment to segment neural transduction"

Connected through hyperedge HE-6 (induced by source paper "monotonic chunkwise attention"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "tacotron towards end to end speech synthesis"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "online segment to segment neural transduction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "learning online alignments with continuous rewards policy gradient"
    - "morphological inflection generation with hard monotonic attention"

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)