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: "deep multi task learning with shared memory"
  Abstract: Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we propose two deep architectures which can be trained jointly on multiple related tasks. More specifically, we augment neural model with an external memory, which is shared by several tasks. Experiments on two groups of text classification tasks show that our proposed architectures can improve the performance of a task with the help of other related tasks.

Connected through hyperedge HE-1 (induced by source paper "a review on neural turing machine"):
  with members:
    - "associative long short term memory"
    - "lstm with working memory"
    - "lie access neural turing machine"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "toward fast and accurate neural chinese word segmentation with multi criteria learning"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "switch lstms for multi criteria chinese word segmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "neural word segmentation with rich pretraining"

Connected through hyperedge HE-3 (induced by source paper "nddr cnn layer wise feature fusing in multi task cnn by neural discriminative dimensionality reduction"):
  with members:
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "squeeze and excitation networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "an overview of multi task learning in deep neural networks"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for universal sentence representations what syntactic and semantic information is captured"):
  with members:
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "end to end neural coreference resolution"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "a generalized recurrent neural architecture for text classification with multi task learning"

Connected through hyperedge HE-5 (induced by source paper "integrating both visual and audio cues for enhanced video caption"):
  with members:
    - "ambient sound provides supervision for visual learning"
    - "multimodal memory modelling for video captioning"
    - "dynamic memory networks for visual and textual question answering"
    - "jointly modeling embedding and translation to bridge video and language"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "a dataset for movie description"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"

Connected through hyperedge HE-6 (induced by source paper "adversarial multi criteria learning for chinese word segmentation"):
  with members:
    - "domain separation networks"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "generating sequences with recurrent neural networks"

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)