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 directed fairness testing"
  Abstract: Fairness is a critical trait in decision making. As machine-learning models are increasingly being used in sensitive application domains (e.g. education and employment) for decision making, it is crucial that the decisions computed by such models are free of unintended bias. But how can we automatically validate the fairness of arbitrary machine-learning models? For a given machine-learning model and a set of sensitive input parameters, our Aequitas approach automatically discovers discriminatory inputs that highlight fairness violation. At the core of Aequitas are three novel strategies to employ probabilistic search over the input space with the objective of uncovering fairness violation. Our Aequitas approach leverages inherent robustness property in common machine-learning models to design and implement scalable test generation methodologies. An appealing feature of our generated test inputs is that they can be systematically added to the training set of the underlying model and improve its fairness. To this end, we design a fully automated module that guarantees to improve the fairness of the model. We implemented Aequitas and we have evaluated it on six state-of-the-art classifiers. Our subjects also include a classifier that was designed with fairness in mind. We show that Aequitas effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of respective models using the generated test inputs. In our evaluation, Aequitas generates up to 70% discriminatory inputs (w.r.t. the total number of inputs generated) and leverages these inputs to improve the fairness up to 94%.

Connected through hyperedge HE-1 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "explaining deep neural networks using spectrum based fault localization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepfault fault localization for deep neural networks"
    - "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"
    - "symbolic execution for deep neural networks"
    - "an empirical study of fault localization families and their combinations"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "model agnostic defence against backdoor attacks in machine learning"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "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"
    - "spectral signatures in backdoor attacks"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-5 (induced by source paper "grammar based directed testing of machine learning systems"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "textbugger generating adversarial text against real world applications"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "webeye automated collection of malicious http traffic"
    - "toward scalable verification for safety critical deep networks"

Connected through hyperedge HE-6 (induced by source paper "callisto entropy based test generation and data quality assessment for machine learning systems"):
  with members:
    - "perturbed model validation a new framework to validate model relevance"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "formal security analysis of neural networks using symbolic intervals"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "software engineering for fairness a case study with hyperparameter optimization"):
  with members:
    - "finding faster configurations using flash"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "hyperparameter optimization for effort estimation"
    - "a convex framework for fair regression"
    - "fairness testing testing software for discrimination"
    - "equality of opportunity in supervised learning"
    - "fairtest discovering unwarranted associations in data driven applications"

Connected through hyperedge HE-8 (induced by source paper "a survey of safety and trustworthiness of deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "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"

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)