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: "quarel a dataset and models for answering questions about qualitative relationships"
  Abstract: Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QuaRel, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, "Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?" We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QuaRel dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel. The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost. The dataset and models are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "careful selection of knowledge to solve open book question answering"):
  with members:
    - "declarative question answering over knowledge bases containing natural language text with answer set programming"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transforming question answering datasets into natural language inference datasets"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "answering science exam questions using query rewriting with background knowledge"
    - "coqa a conversational question answering challenge"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-2 (induced by source paper "reasoning over paragraph effects in situations"):
  with members:
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"

Connected through hyperedge HE-3 (induced by source paper "zero shot semantic parsing for instructions"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "neural semantic parsing over multiple knowledge bases"
    - "cross domain semantic parsing via paraphrasing"
    - "simpler context dependent logical forms via model projections"
    - "compositional semantic parsing on semi structured tables"
    - "lambda dependency based compositional semantics"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-4 (induced by source paper "quartz an open domain dataset of qualitative relationship questions"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "allennlp a deep semantic natural language processing platform"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "simple and effective multi paragraph reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "a generate validate approach to answering questions about qualitative relationships"):
  with members:
    - "declarative question answering over knowledge bases containing natural language text with answer set programming"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhanced lstm for natural language inference"
    - "a large annotated corpus for learning natural language inference"

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)