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: "aser a large scale eventuality knowledge graph"
  Abstract: Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both human and extrinsic evaluations demonstrate the quality and effectiveness of ASER.

Connected through hyperedge HE-1 (induced by source paper "towards a framework for visual intelligence in service robotics epistemic requirements and gap analysis"):
  with members:
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "knowledge is never enough towards web aided deep open world recognition"
    - "continual lifelong learning with neural networks a review"
    - "a survey of knowledge representation in service robotics"
    - "integrating knowledge and reasoning in image understanding"
    - "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"
    - "visual semantic information pursuit a survey"

Connected through hyperedge HE-2 (induced by source paper "on the role of conceptualization in commonsense knowledge graph construction"):
  with members:
    - "kg bert bert for knowledge graph completion"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "roberta a robustly optimized bert pretraining approach"
    - "a simple method for commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "knowledge aware pronoun coreference resolution"):
  with members:
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "the hard core coreference corpus removing gender and number cues for difficult pronominal anaphora resolution"
    - "end to end neural coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "incorporating context and external knowledge for pronoun coreference resolution"):
  with members:
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "the hard core coreference corpus removing gender and number cues for difficult pronominal anaphora resolution"
    - "end to end neural coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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.AI(Artificial Intelligence)