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: "output range analysis for deep neural networks"
  Abstract: Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, we study a specific verification problem of computing a guaranteed range for the output of a deep neural network given a set of inputs represented as a convex polyhedron. Range estimation is a key primitive for verifying deep NNs. We present an efficient range estimation algorithm that uses a combination of local search and linear programming problems to efficiently find the maximum and minimum values taken by the outputs of the NN over the given input set. In contrast to recently proposed "monolithic" optimization approaches, we use local gradient descent to repeatedly find and eliminate local minima of the function. The final global optimum is certified using a mixed integer programming instance. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate the effectiveness of the proposed approach for verification of NNs used in automated control as well as those used in classification.

Connected through hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "seeing what a gan cannot generate"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"

Connected through hyperedge HE-2 (induced by source paper "reachability analysis for feed forward neural networks using face lattices"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of cnn based perception systems"
    - "efficient neural network robustness certification with general activation functions"
    - "efficient formal safety analysis of neural networks"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "bounding and counting linear regions of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretical evidence for adversarial robustness through randomization"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-5 (induced by source paper "certified adversarial robustness via randomized smoothing"):
  with members:
    - "certifiably robust interpretation in deep learning"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-6 (induced by source paper "optimization and abstraction a synergistic approach for analyzing neural network robustness"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "an overview of vulnerabilities of voice controlled systems"
    - "robust physical world attacks on deep learning models"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards practical verification of machine learning the case of computer vision systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "robust physical world attacks on machine learning models"

Connected through hyperedge HE-7 (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"
    - "automated verification of neural networks advances challenges and perspectives"
    - "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"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-8 (induced by source paper "an abstraction based framework for neural network verification"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "the three pillars of machine programming"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards proving the adversarial robustness of deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"

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)