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: "the octagon abstract domain"
  Abstract: This article presents a new numerical abstract domain for static analysis by abstract interpretation. It extends a former numerical abstract domain based on Difference-Bound Matrices and allows us to represent invariants of the form (+/-x+/-y<=c), where x and y are program variables and c is a real constant. We focus on giving an efficient representation based on Difference-Bound Matrices - O(n2) memory cost, where n is the number of variables - and graph-based algorithms for all common abstract operators - O(n3) time cost. This includes a normal form algorithm to test equivalence of representation and a widening operator to compute least fixpoint approximations.

Connected through hyperedge HE-1 (induced by source paper "reludiff differential verification of deep neural networks"):
  with members:
    - "towards compact and robust deep neural networks"
    - "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"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient formal safety analysis of neural networks"

Connected through hyperedge HE-2 (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"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "nn dependability kit engineering neural networks for safety critical autonomous driving systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "runtime monitoring neuron activation patterns"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "verismart a highly precise safety verifier for ethereum smart contracts"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "online detection of effectively callback free objects with applications to smart contracts"
    - "gastap a gas analyzer for smart contracts"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "formal specification and verification of smart contracts for azure blockchain"
    - "vandal a scalable security analysis framework for smart contracts"
    - "securify practical security analysis of smart contracts"

Connected through hyperedge HE-5 (induced by source paper "abstract interpretation under speculative execution"):
  with members:
    - "mitigating power side channels during compilation"
    - "eliminating timing side channel leaks using program repair"
    - "adversarial symbolic execution for detecting concurrency related cache timing leaks"
    - "spectre attacks exploiting speculative execution"
    - "canal a cache timing analysis framework via llvm transformation"
    - "time protection the missing os abstraction"
    - "modular verification of interrupt driven software"
    - "thread modular static analysis for relaxed memory models"

Connected through hyperedge HE-6 (induced by source paper "compositional verification of heap manipulating programs through property guided learning"):
  with members:
    - "sling using dynamic analysis to infer program invariants in separation logic"
    - "concolic testing heap manipulating programs"
    - "enhancing symbolic execution of heap based programs with separation logic for test input generation"
    - "gated graph sequence neural networks"
    - "fully automated shape analysis based on forest automata"
    - "learning universally quantified invariants of linear data structures"
    - "cpachecker a tool for configurable software verification"

Connected through hyperedge HE-7 (induced by source paper "outside the box abstraction based monitoring of neural networks"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "runtime monitoring neuron activation patterns"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "end to end learning for self driving cars"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-8 (induced by source paper "overfitting in synthesis theory and practice extended version"):
  with members:
    - "sygus comp 2018 results and analysis"
    - "horn ice learning for synthesizing invariantsand contracts"
    - "webrelate integrating web data with spreadsheets using examples"
    - "loopinvgen a loop invariant generator based on precondition inference"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"
    - "a theory of formal synthesis via inductive learning"

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.PL(Programming Languages)