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 "neural symbolic argumentation mining an argument in favour of deep learning and reasoning"):
  Abstract: Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap towards performing advanced reasoning tasks. We illustrate how neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.

Includes members:
  Member "deep learning for sentiment analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos")
  Member "argument component classification for classroom discussions":
    also connected through other hyperedges:
      (none)
  Member "multi task learning for argumentation mining in low resource settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "what is the essence of a claim cross domain claim identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tacam topic and context aware argument mining")
  Member "deep residual learning for weakly supervised relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks")
  Member "argument mining with structured svms and rnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "logic tensor networks deep learning and logical reasoning from data and knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "defeasible logic programming an argumentative approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cognitive argumentation and the suppression task")

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)