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: "argument mining with structured svms and rnns"
  Abstract: We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.

Connected through hyperedge HE-1 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-2 (induced by source paper "a dataset independent set of baselines for relation prediction in argument mining"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argumentation mining in user generated web discourse"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "autoencoder feature selector"
    - "parsing argumentation structures in persuasive essays"
    - "a decomposable attention model for natural language inference"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "exploring the role of prior beliefs for argument persuasion"
    - "neural argument generation augmented with externally retrieved evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "neural network based abstract generation for opinions and arguments"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-4 (induced by source paper "argument mining for understanding peer reviews"):
  with members:
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "argumentation mining in user generated web discourse"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "understanding and detecting supporting arguments of diverse types"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "neural symbolic argumentation mining an argument in favour of deep learning and reasoning"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "argument component classification for classroom discussions"
    - "multi task learning for argumentation mining in low resource settings"
    - "what is the essence of a claim cross domain claim identification"
    - "deep residual learning for weakly supervised relation extraction"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "defeasible logic programming an argumentative approach"

Connected through hyperedge HE-6 (induced by source paper "neural argument generation augmented with externally retrieved evidence"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "understanding and detecting supporting arguments of diverse types"
    - "sequence to sequence learning as beam search optimization"
    - "neural network based abstract generation for opinions and arguments"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "argument component classification for classroom discussions"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "argumentation mining in user generated web discourse"
    - "predicting specificity in classroom discussion"
    - "what is the essence of a claim cross domain claim identification"
    - "learning to learn by gradient descent by gradient descent"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-8 (induced by source paper "attentive interaction model modeling changes in view in argumentation"):
  with members:
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "personalized emphasis framing for persuasive message generation"
    - "a large annotated corpus for learning natural language inference"
    - "on the properties of neural machine translation encoder decoder approaches"

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)