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: "robust argument unit recognition and classification"
  Abstract: Argument mining is generally performed on the sentence-level -- it is assumed that an entire sentence (not parts of it) corresponds to an argument. In this paper, we introduce the new task of Argument unit Recognition and Classification (ARC). In ARC, an argument is generally a part of a sentence -- a more realistic assumption since several different arguments can occur in one sentence and longer sentences often contain a mix of argumentative and non-argumentative parts. Recognizing and classifying the spans that correspond to arguments makes ARC harder than previously defined argument mining tasks. We release ARC-8, a new benchmark for evaluating the ARC task. We show that token-level annotations for argument units can be gathered using scalable methods. ARC-8 contains 25\% more arguments than a dataset annotated on the sentence-level would. We cast ARC as a sequence labeling task, develop a number of methods for ARC sequence tagging and establish the state of the art for ARC-8. A focus of our work is robustness: both robustness against errors in sentence identification (which are frequent for noisy text) and robustness against divergence in training and test data.

Connected through hyperedge HE-1 (induced by source paper "tacam topic and context aware argument mining"):
  with members:
    - "deep contextualized word representations"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "what is the essence of a claim cross domain claim identification"
    - "attention is all you need"
    - "understanding and detecting supporting arguments of diverse types"

Connected through hyperedge HE-2 (induced by source paper "a bayesian approach for sequence tagging with crowds"):
  with members:
    - "leveraging crowdsourcing data for deep active learning an application learning intents in alexa"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "neural architectures for named entity recognition"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)