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: "lyrics a general interface layer to integrate ai and deep learning"
  Abstract: In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference. Therefore, there is a clear need for the definition of a general and tight integration between low-level tasks, processing sensorial data that can be effectively elaborated using deep learning techniques, and the logic reasoning that allows humans to take decisions in complex environments. This paper presents LYRICS, a generic interface layer for AI, which is implemented in TersorFlow (TF). LYRICS provides an input language that allows to define arbitrary First Order Logic (FOL) background knowledge. The predicates and functions of the FOL knowledge can be bound to any TF computational graph, and the formulas are converted into a set of real-valued constraints, which participate to the overall optimization problem. This allows to learn the weights of the learners, under the constraints imposed by the prior knowledge. The framework is extremely general as it imposes no restrictions in terms of which models or knowledge can be integrated. In this paper, we show the generality of the approach showing some use cases of the presented language, including generative models, logic reasoning, model checking and supervised learning.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "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"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-2 (induced by source paper "neural markov logic networks"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "deepproblog neural probabilistic logic programming"
    - "inference learning and population size projectivity for srl models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "how powerful are graph neural networks"
    - "end to end differentiable proving"

Connected through hyperedge HE-3 (induced by source paper "learning and t norms theory"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "tensorflow a system for large scale machine learning"
    - "hinge loss markov random fields and probabilistic soft logic"

Connected through hyperedge HE-4 (induced by source paper "a game method for improving the interpretability of convolution neural network"):
  with members:
    - "interpreting cnns via decision trees"
    - "on a convex logic fragment for learning and reasoning"
    - "interpreting deep visual representations via network dissection"
    - "interpretable convolutional neural networks"
    - "growing interpretable part graphs on convnets via multi shot learning"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "constraint based visual generation"):
  with members:
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial sets for regularising neural link predictors"
    - "harnessing deep neural networks with logic rules"
    - "lifted rule injection for relation embeddings"
    - "coupled generative adversarial networks"
    - "tensorlog a differentiable deductive database"
    - "tensorflow a system for large scale machine learning"

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.LG(Machine Learning)