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: "lifted relational neural networks"
  Abstract: We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.

Connected through hyperedge HE-1 (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-2 (induced by source paper "non parametric learning of lifted restricted boltzmann machines"):
  with members:
    - "structure learning for relational logistic regression an ensemble approach"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning to compare relation network for few shot learning"
    - "column networks for collective classification"
    - "learning convolutional neural networks for graphs"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "integrating deep learning with logic fusion for information extraction"):
  with members:
    - "neural logic machines"
    - "deepproblog neural probabilistic logic programming"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "adversarial training for multi context joint entity and relation extraction"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "human guided learning of column networks augmenting deep learning with advice"):
  with members:
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "column networks for collective classification"
    - "semi supervised classification with graph convolutional networks"
    - "interaction networks for learning about objects relations and physics"
    - "beyond short snippets deep networks for video classification"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deeplogic towards end to end differentiable logical reasoning"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "can neural networks understand logical entailment"
    - "learning explanatory rules from noisy data"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-8 (induced by source paper "logical rule induction and theory learning using neural theorem proving"):
  with members:
    - "deepproblog neural probabilistic logic programming"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "logic tensor networks for semantic image interpretation"
    - "tensorlog a differentiable deductive database"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"

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)