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: "commonsense mining as knowledge base completion a study on the impact of novelty"
  Abstract: Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by the recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel.

Connected through hyperedge HE-1 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "ranking and selecting multi hop knowledge paths to better predict human needs"
    - "modeling naive psychology of characters in simple commonsense stories"

Connected through hyperedge HE-2 (induced by source paper "commonsense knowledge base completion with structural and semantic context"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "language models as knowledge bases"
    - "probing the need for visual context in multimodal machine translation"
    - "commonsense knowledge mining from pretrained models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "answering science exam questions using query rewriting with background knowledge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-3 (induced by source paper "commonsense knowledge mining from pretrained models"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "a simple method for commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "convolutional 2d knowledge graph embeddings"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"

Connected through hyperedge HE-4 (induced by source paper "machine common sense concept paper"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "probing physics knowledge using tools from developmental psychology"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "modeling semantic plausibility by injecting world knowledge"
    - "event2mind commonsense inference on events intents and reactions"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "verb physics relative physical knowledge of actions and objects"
    - "simulating action dynamics with neural process networks"

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)