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: "a semantic loss function for deep learning with symbolic knowledge"
  Abstract: This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that our semantic loss function effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.

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 "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "counterfactuals uncover the modular structure of deep generative models"
    - "on relaxing determinism in arithmetic circuits"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "budgeted experiment design for causal structure learning"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "on constrained open world probabilistic databases"
    - "learning logistic circuits"
    - "on relaxing determinism in arithmetic circuits"
    - "imposing hard constraints on deep networks promises and limitations"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "elaborating on learned demonstrations with temporal logic specifications"):
  with members:
    - "specification patterns for robotic missions"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "imitation learning of robot policies by combining language vision and demonstration"
    - "residual policy learning"
    - "guiding policies with language via meta learning"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "generative adversarial imitation learning"
    - "robust linear temporal logic"

Connected through hyperedge HE-7 (induced by source paper "injective domain knowledge in neural networks for transprecision computing"):
  with members:
    - "augmenting neural networks with first order logic"
    - "comparing knowledge based reinforcement learning to neural networks in a strategy game"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "augmenting neural networks with first order logic"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "backpropagating through structured argmax using a spigot"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "towards semi supervised learning for deep semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially regularising neural nli models to integrate logical background 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)