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: "implicit discourse relation classification via multi task neural networks"
  Abstract: Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.

Connected through hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "spectral normalization for generative adversarial networks"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "cross lingual distillation for text classification"
    - "aspect augmented adversarial networks for domain adaptation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "jointly learning semantic parser and natural language generator via dual information maximization"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "code2seq generating sequences from structured representations of code"
    - "triangle generative adversarial networks"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-3 (induced by source paper "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "memorizing all for implicit discourse relation recognition"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "deep biaffine attention for neural dependency parsing"
    - "language modeling with gated convolutional networks"
    - "key value memory networks for directly reading documents"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"

Connected through hyperedge HE-5 (induced by source paper "discofuse a large scale dataset for discourse based sentence fusion"):
  with members:
    - "training tips for the transformer model"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "attention is all you need"
    - "automatic prediction of discourse connectives"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural machine translation of rare words with subword units"
    - "abstractive multi document summarization via phrase selection and merging"

Connected through hyperedge HE-6 (induced by source paper "learning to explicitate connectives with seq2seq network for implicit discourse relation classification"):
  with members:
    - "acquiring annotated data with cross lingual explicitation for implicit discourse relation classification"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "adversarial connective exploiting networks for implicit discourse relation classification"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "effective approaches to attention based neural machine translation"
    - "semi supervised sequence learning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "adversarial connective exploiting networks for implicit discourse relation classification"):
  with members:
    - "toward controlled generation of text"
    - "fast and accurate neural word segmentation for chinese"
    - "variational neural discourse relation recognizer"
    - "harnessing deep neural networks with logic rules"
    - "improved techniques for training gans"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"

Connected through hyperedge HE-8 (induced by source paper "iss mult intelligent sample selection for multi task learning in question answering"):
  with members:
    - "squad 100 000 questions for machine comprehension of text"
    - "selqa a new benchmark for selection based question answering"
    - "how transferable are features in deep neural networks"
    - "deep learning for answer sentence selection"
    - "distributed representations of words and phrases and their compositionality"

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)