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: "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
  Abstract: Question Answering for complex questions is often modeled as a graph construction or traversal task, where a solver must build or traverse a graph of facts that answer and explain a given question. This "multi-hop" inference has been shown to be extremely challenging, with few models able to aggregate more than two facts before being overwhelmed by "semantic drift", or the tendency for long chains of facts to quickly drift off topic. This is a major barrier to current inference models, as even elementary science questions require an average of 4 to 6 facts to answer and explain. In this work we empirically characterize the difficulty of building or traversing a graph of sentences connected by lexical overlap, by evaluating chance sentence aggregation quality through 9,784 manually-annotated judgments across knowledge graphs built from three free-text corpora (including study guides and Simple Wikipedia). We demonstrate semantic drift tends to be high and aggregation quality low, at between 0.04% and 3%, and highlight scenarios that maximize the likelihood of meaningfully combining information.

Connected through hyperedge HE-1 (induced by source paper "qasc a dataset for question answering via sentence composition"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "improving question answering with external knowledge"
    - "question answering as global reasoning over semantic abstractions"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what s missing a knowledge gap guided approach for multi hop question answering"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "from recognition to cognition visual commonsense reasoning"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-2 (induced by source paper "understanding dataset design choices for multi hop reasoning"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

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"
    - "e snli natural language inference with natural language explanations"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "question answering by reasoning across documents with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "asu at textgraphs 2019 shared task explanation regeneration using language models and iterative re ranking"):
  with members:
    - "careful selection of knowledge to solve open book question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised textual grounding linking words to image concepts"
    - "race large scale reading comprehension dataset from examinations"

Connected through hyperedge HE-5 (induced by source paper "red dragon ai at textgraphs 2019 shared task language model assisted explanation generation"):
  with members:
    - "multi class hierarchical question classification for multiple choice science exams"
    - "roberta a robustly optimized bert pretraining approach"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "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)