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: "deep learning for ontology reasoning"
  Abstract: In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. To this end, we introduce a new model for statistical relational learning that is built upon deep recursive neural networks, and give experimental evidence that it can easily compete with, or even outperform, existing logic-based reasoners on the task of ontology reasoning. More precisely, we compared our implemented system with one of the best logic-based ontology reasoners at present, RDFox, on a number of large standard benchmark datasets, and found that our system attained high reasoning quality, while being up to two orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "explainable deep rdfs reasoner"):
  with members:
    - "reasoning over rdf knowledge bases using deep learning"
    - "knowledge graph completion via complex tensor factorization"
    - "opening the black box of deep neural networks via information"
    - "holographic embeddings of knowledge graphs"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "a boxology of design patterns for hybrid learning and reasoning systems"):
  with members:
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "reasoning over rdf knowledge bases using deep learning"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "knowledge based transfer learning explanation"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-3 (induced by source paper "sld resolution reduction of second order horn fragments technical report"):
  with members:
    - "logical rule induction and theory learning using neural theorem proving"
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "end to end differentiable proving"
    - "minimization for generalized boolean formulas"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy in logic i cnf propositional formulae"

Connected through hyperedge HE-4 (induced by source paper "what do we need to build explainable ai systems for the medical domain"):
  with members:
    - "cross modal deep metric learning with multi task regularization"
    - "towards the augmented pathologist challenges of explainable ai in digital pathology"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "a glass box interactive machine learning approach for solving np hard problems with the human in the loop"
    - "generating visual explanations"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)