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: "logic tensor networks deep learning and logical reasoning from data and knowledge"
  Abstract: We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's tensorflow primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge completion.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks"
    - "learning and t norms theory"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "deepproblog neural probabilistic logic programming"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "recurrent independent mechanisms"
    - "deep learning for symbolic mathematics"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"

Connected through hyperedge HE-4 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks"):
  with members:
    - "augmenting neural networks with first order logic"
    - "neural theorem provers do not learn rules without exploration"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "semantically regularized logic graph embeddings"
    - "pytorch an imperative style high performance deep learning library"
    - "backpropagating through structured argmax using a spigot"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-5 (induced by source paper "teaching the old dog new tricks supervised learning with constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "deepproblog neural probabilistic logic programming"
    - "a convex framework for fair regression"
    - "end to end differentiable proving"
    - "equality of opportunity in supervised learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a confidence based approach for balancing fairness and accuracy"
    - "efficient piecewise training of deep structured models for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "injecting domain knowledge in neural networks a controlled experiment on a constrained problem"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "learning explanatory rules from noisy data"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "logic and the 2 simplicial transformer"):
  with members:
    - "neural logic machines"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "an investigation of model free planning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational recurrent neural networks"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (induced by source paper "learning invariants through soft unification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an attentive survey of attention models"
    - "learning explanatory rules from noisy data"
    - "explanation in artificial intelligence insights from the social sciences"
    - "end to end differentiable proving"
    - "harnessing deep neural networks with logic rules"
    - "tracking the world state with recurrent entity networks"
    - "the mythos of model interpretability"

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)