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: "dimensions of neural symbolic integration a structured survey"
  Abstract: Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.

Connected through hyperedge HE-1 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence"):
  with members:
    - "relational neural machines"
    - "neural logic machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "playgol learning programs through play"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"

Connected through hyperedge HE-2 (induced by source paper "directions for explainable knowledge enabled systems"):
  with members:
    - "explaining explanations in ai"
    - "explaining explanations an overview of interpretability of machine learning"
    - "blockchain and artificial intelligence"
    - "explanation in artificial intelligence insights from the social sciences"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "accountability of ai under the law the role of explanation"
    - "the blockchain a new framework for robotic swarm systems"
    - "the mythos of model interpretability"

Connected through hyperedge HE-3 (induced by source paper "probabilistic neural symbolic models for interpretable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evalai towards better evaluation systems for ai agents"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "houdini lifelong learning as program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"

Connected through hyperedge HE-4 (induced by source paper "robust deep neural networks inspired by fuzzy logic"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "understanding adversarial robustness the trade off between minimum and average margin"
    - "adversarial training versus weight decay"
    - "sparse dnns with improved adversarial robustness"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-5 (induced by source paper "semantic referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  with members:
    - "location augmentation for cnn"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep learning in remote sensing a review"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "explaining trained neural networks with semantic web technologies first steps"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "relating input concepts to convolutional neural network decisions"
    - "what does explainable ai really mean a new conceptualization of perspectives"

Connected through hyperedge HE-6 (induced by source paper "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "deepproblog neural probabilistic logic programming"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "a simple neural network module for relational reasoning"
    - "tensorlog deep learning meets probabilistic dbs"

Connected through hyperedge HE-7 (induced by source paper "graph colouring meets deep learning effective graph neural network models for combinatorial problems"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "recurrent relational networks for complex relational reasoning"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "combining the k cnf and xor phase transitions"

Connected through hyperedge HE-8 (induced by source paper "logic negation with spiking neural p systems"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "neural symbolic learning and reasoning a survey and interpretation"

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)