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: "are you convinced choosing the more convincing evidence with a siamese network"
  Abstract: With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.

Connected through hyperedge HE-1 (induced by source paper "corpus wide argument mining a working solution"):
  with members:
    - "fast end to end wikification"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"
    - "an empirical evaluation of various deep learning architectures for bi sequence classification tasks"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "a large scale dataset for argument quality ranking construction and analysis"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "automatic argument quality assessment new datasets and methods"
    - "corpus wide argument mining a working solution"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"

Connected through hyperedge HE-3 (induced by source paper "automatic argument quality assessment new datasets and methods"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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.LG(Machine Learning)