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 verification of neural networks advances challenges and perspectives"
  Abstract: Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safety- and security-related contexts, wherein assurance about networks' performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.

Connected through hyperedge HE-1 (induced by source paper "verification of neural networks enhancing scalability through pruning"):
  with members:
    - "scaling provable adversarial defenses"
    - "sparse dnns with improved adversarial robustness"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "safety and trustworthiness of deep neural networks a survey"
    - "rethinking the value of network pruning"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "learning efficient convolutional networks through network slimming"
    - "an approach to reachability analysis for feed forward relu neural networks"

Connected through hyperedge HE-2 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

Connected through hyperedge HE-3 (induced by source paper "a logic based framework leveraging neural networks for studying the evolution of neurological disorders"):
  with members:
    - "the seventh answer set programming competition design and results"
    - "the external interface for extending wasp"
    - "asp core 2 input language format"
    - "a comprehensive survey on graph neural networks"
    - "interpreting cnns via decision trees"
    - "constraint answer set programming without grounding"
    - "visual interpretability for deep learning a survey"
    - "formal verification of cnn based perception systems"

Connected through hyperedge HE-4 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards proving the adversarial robustness of deep neural networks"
    - "output range analysis for deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-5 (induced by source paper "algorithms for verifying deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "juliareach a toolbox for set based reachability"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "efficient neural network robustness certification with general activation functions"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "formal verification of neural network controlled autonomous systems"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "agi safety literature review"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"

Connected through hyperedge HE-7 (induced by source paper "verification of recurrent neural networks through rule extraction"):
  with members:
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "on multiplicative integration with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

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)