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: "same representation different attentions shareable sentence representation learning from multiple tasks"
  Abstract: Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.

Connected through hyperedge HE-1 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "dual supervised learning for natural language understanding and generation"
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "dual supervised learning"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-2 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"

Connected through hyperedge HE-3 (induced by source paper "tdam a topic dependent attention model for sentiment analysis"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning structured text representations"
    - "attention is all you need"
    - "the consciousness prior"
    - "adversarial multi task learning for text classification"
    - "multi task label embedding for text classification"

Connected through hyperedge HE-4 (induced by source paper "show translate and tell"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "linking image and text with 2 way nets"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "multi task learning with multi view attention for answer selection and knowledge base question answering"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved neural relation detection for knowledge base question answering"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "cross temporal recurrent networks for ranking question answer pairs"
    - "attentive pooling networks"
    - "squad 100 000 questions for machine comprehension of text"

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)