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: "on a convex logic fragment for learning and reasoning"
  Abstract: In this paper we introduce the convex fragment of {\L}ukasiewicz Logic and discuss its possible applications in different learning schemes. Indeed, the provided theoretical results are highly general, because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be equivalent to a set of linear constraints. Within this framework, we are able to formulate learning with kernel machines as well as collective classification as a quadratic programming problem.

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"
    - "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"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-2 (induced by source paper "learning and t norms theory"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "deepproblog neural probabilistic logic programming"
    - "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-3 (induced by source paper "a game method for improving the interpretability of convolution neural network"):
  with members:
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "interpreting cnns via decision trees"
    - "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-4 (induced by source paper "on the relation between loss functions and t norms"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "hinge loss markov random fields and probabilistic soft logic"

Connected through hyperedge HE-5 (induced by source paper "conditions for unnecessary logical constraints in kernel machines"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "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.LO(Logic in Computer Science)