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: "socialiqa commonsense reasoning about social interactions"
  Abstract: We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could hear"). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).

Connected through hyperedge HE-1 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "learning to denoise distantly labeled data for entity typing"

Connected through hyperedge HE-2 (induced by source paper "cosmos qa machine reading comprehension with contextual commonsense reasoning"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "annotation artifacts in natural language inference data"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"

Connected through hyperedge HE-3 (induced by source paper "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"):
  with members:
    - "abductive commonsense reasoning"
    - "careful selection of knowledge to solve open book question answering"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikihow a large scale text summarization dataset"

Connected through hyperedge HE-4 (induced by source paper "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"):
  with members:
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "annotation artifacts in natural language inference data"
    - "a simple method for commonsense reasoning"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "a knowledge hunting framework for common sense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "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)