human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  Abstract: Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks. Deep learning that primarily constitutes neural computing stream in AI has shown significant advances in probabilistically learning latent patterns using a multi-layered network of computational nodes (i.e., neurons/hidden units). Structured knowledge that underlies symbolic computing approaches and often supports reasoning, has also seen significant growth in recent years, in the form of broad-based (e.g., DBPedia, Yago) and domain, industry or application specific knowledge graphs. A common substrate with careful integration of the two will raise opportunities to develop neuro-symbolic learning approaches for AI, where conceptual and probabilistic representations are combined. As the incorporation of external knowledge will aid in supervising the learning of features for the model, deep infusion of representational knowledge from knowledge graphs within hidden layers will further enhance the learning process. Although much work remains, we believe that knowledge graphs will play an increasing role in developing hybrid neuro-symbolic intelligent systems (bottom-up deep learning with top-down symbolic computing) as well as in building explainable AI systems for which knowledge graphs will provide scaffolding for punctuating neural computing. In this position paper, we describe our motivation for such a neuro-symbolic approach and framework that combines knowledge graph and neural networks.

Includes members:
  Member "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain knowledge aided explainable artificial intelligence for intrusion detection and response")
  Member "ernie enhanced representation through knowledge integration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "predictive analysis on twitter techniques and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate")
  Member "knowledge aware conversation generation with explainable reasoning on augmented graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate":
    also connected through other hyperedges:
      (none)
  Member "k bert enabling language representation with knowledge graph":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pre training for query rewriting in a spoken language understanding system")
  Member "ai reasoning systems pac and applied methods":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)