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: "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"
  Abstract: End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. We present experiments on conversational speech recognition where we use lower-level tasks, such as phoneme recognition, in a multitask training approach with an encoder-decoder model for direct character transcription. We compare multiple types of lower-level tasks and analyze the effects of the auxiliary tasks. Our results on the Switchboard corpus show that this approach improves recognition accuracy over a standard encoder-decoder model on the Eval2000 test set.

Connected through hyperedge HE-1 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "monocular fisheye camera depth estimation using sparse lidar supervision"
    - "auxiliary tasks in multi task learning"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "monocular depth estimation by learning from heterogeneous datasets"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"

Connected through hyperedge HE-3 (induced by source paper "self supervised generalisation with meta auxiliary learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "cinic 10 is not imagenet or cifar 10"
    - "auxiliary tasks in multi task learning"
    - "end to end multi task learning with attention"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "on the contributions of visual and textual supervision in low resource semantic speech retrieval"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "visually grounded cross lingual keyword spotting in speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"

Connected through hyperedge HE-5 (induced by source paper "sentiment tagging with partial labels using modular architectures"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "mattnet modular attention network for referring expression comprehension"
    - "learning to parse and translate improves neural machine translation"
    - "learning to reason end to end module networks for visual question answering"
    - "empower sequence labeling with task aware neural language model"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-6 (induced by source paper "end to end video captioning with multitask reinforcement learning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-7 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"

Connected through hyperedge HE-8 (induced by source paper "pseudo task augmentation from deep multitask learning to intratask sharing and back"):
  with members:
    - "evolving deep neural networks"
    - "learned in translation contextualized word vectors"
    - "learning multiple visual domains with residual adapters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"

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)