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: "variational neural discourse relation recognizer"
  Abstract: Implicit discourse relation recognition is a crucial component for automatic discourselevel analysis and nature language understanding. Previous studies exploit discriminative models that are built on either powerful manual features or deep discourse representations. In this paper, instead, we explore generative models and propose a variational neural discourse relation recognizer. We refer to this model as VarNDRR. VarNDRR establishes a directed probabilistic model with a latent continuous variable that generates both a discourse and the relation between the two arguments of the discourse. In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound. This allows VarNDRR to be trained with standard stochastic gradient methods. Experiments on the benchmark data set show that VarNDRR can achieve comparable results against stateof- the-art baselines without using any manual features.

Connected through hyperedge HE-1 (induced by source paper "linguistic versus latent relations for modeling coherent flow in paragraphs"):
  with members:
    - "describing a knowledge base"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "detecting and explaining causes from text for a time series event"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-2 (induced by source paper "neural simile recognition with cyclic multitask learning and local attention"):
  with members:
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "variational recurrent neural machine translation"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"
    - "variational neural machine translation"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-3 (induced by source paper "learning better discourse representation for implicit discourse relation recognition via attention networks"):
  with members:
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "battrae bidimensional attention based recursive autoencoders for learning bilingual phrase embeddings"
    - "end to end memory networks"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask me anything dynamic memory networks for natural language processing"
    - "recurrent models of visual attention"

Connected through hyperedge HE-4 (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"
    - "implicit discourse relation classification via multi task neural networks"
    - "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"

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)