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: "automated proof synthesis for propositional logic with deep neural networks"
  Abstract: This work explores the application of deep learning, a machine learning technique that uses deep neural networks (DNN) in its core, to an automated theorem proving (ATP) problem. To this end, we construct a statistical model which quantifies the likelihood that a proof is indeed a correct one of a given proposition. Based on this model, we give a proof-synthesis procedure that searches for a proof in the order of the likelihood. This procedure uses an estimator of the likelihood of an inference rule being applied at each step of a proof. As an implementation of the estimator, we propose a proposition-to-proof architecture, which is a DNN tailored to the automated proof synthesis problem. To empirically demonstrate its usefulness, we apply our model to synthesize proofs of propositional logic. We train the proposition-to-proof model using a training dataset of proposition-proof pairs. The evaluation against a benchmark set shows the very high accuracy and an improvement to the recent work of neural proof synthesis.

Connected through hyperedge HE-1 (induced by source paper "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "verifying security protocols using dynamic strategies"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "formal analysis of 5g authentication"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "deepmath deep sequence models for premise selection"
    - "a comprehensive formal security analysis of oauth 2 0"
    - "end to end memory networks"
    - "a hybrid analysis for security protocols with state"

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)