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: "the argument reasoning comprehension task identification and reconstruction of implicit warrants"
  Abstract: Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

Connected through hyperedge HE-1 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "mmm multi stage multi task learning for multi choice reading comprehension"
    - "evalai towards better evaluation systems for ai agents"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cosmos qa machine reading comprehension with contextual commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"

Connected through hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Connected through hyperedge HE-3 (induced by source paper "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "automated fact checking task formulations methods and future directions"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "e snli natural language inference with natural language explanations"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"

Connected through hyperedge HE-4 (induced by source paper "evaluating commonsense in pre trained language models"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "does it make sense and why a pilot study for sense making and explanation"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "linguistic knowledge and transferability of contextual representations"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-5 (induced by source paper "when choosing plausible alternatives clever hans can be clever"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "learning to rank for plausible plausibility"
    - "on the validity of self attention as explanation in transformer models"
    - "multi task deep neural networks for natural language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "probing neural network comprehension of natural language arguments"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nlitrans at semeval 2018 task 12 transfer of semantic knowledge for argument comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-7 (induced by source paper "the evolution of argumentation mining from models to social media and emerging tools"):
  with members:
    - "claudette an automated detector of potentially unfair clauses in online terms of service"
    - "argumentation mining exploiting multiple sources and background knowledge"
    - "multi task learning for argumentation mining in low resource settings"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "argumentation mining in user generated web discourse"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-8 (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"
    - "are you convinced choosing the more convincing evidence with a siamese network"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)