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: "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
  Abstract: Deep neural networks (DNNs) have been shown lack of robustness for the vulnerability of their classification to small perturbations on the inputs. This has led to safety concerns of applying DNNs to safety-critical domains. Several verification approaches have been developed to automatically prove or disprove safety properties of DNNs. However, these approaches suffer from either the scalability problem, i.e., only small DNNs can be handled, or the precision problem, i.e., the obtained bounds are loose. This paper improves on a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. More specifically, the values of neurons are represented symbolically and propagated forwardly from the input layer to the output layer, on top of abstract domains. We show that our approach can achieve significantly higher precision and thus can prove more properties than using only abstract domains. Moreover, we show that the bounds derived from our approach on the hidden neurons, when applied to a state-of-the-art SMT based verification tool, can improve its performance. We implement our approach into a software tool and validate it over a few DNNs trained on benchmark datasets such as MNIST, etc.

Connected through hyperedge HE-1 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a provable defense for deep residual networks"
    - "a survey on bias and fairness in machine learning"
    - "the efficacy of shield under different threat models"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards robust direct perception networks for automated driving"):
  with members:
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "conditional affordance learning for driving in urban environments"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "concolic testing for deep neural networks"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-3 (induced by source paper "algorithms for verifying deep neural networks"):
  with members:
    - "juliareach a toolbox for set based reachability"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "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-4 (induced by source paper "towards safety verification of direct perception neural networks"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "safety and trustworthiness of deep neural networks a survey"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "maximum resilience of artificial neural networks"
    - "formal verification of piece wise linear feed forward neural networks"
    - "opening the black box of deep neural networks via information"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a survey of safety and trustworthiness of deep neural networks"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"

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)