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: "towards accountable ai hybrid human machine analyses for characterizing system failure"
  Abstract: As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging.

Connected through hyperedge HE-1 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "what clinicians want contextualizing explainable machine learning for clinical end use"):
  with members:
    - "model cards for model reporting"
    - "attention is not explanation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "opportunities in machine learning for healthcare"
    - "clinicalvis supporting clinical task focused design evaluation"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"

Connected through hyperedge HE-4 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "visualbackprop efficient visualization of cnns"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "the elephant in the room"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"

Connected through hyperedge HE-5 (induced by source paper "founding the domain of ai forensics"):
  with members:
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "emergence of addictive behaviors in reinforcement learning agents"
    - "sok security and privacy in machine learning"
    - "a psychopathological approach to safety engineering in ai and agi"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-6 (induced by source paper "toward fairness in ai for people with disabilities a research roadmap"):
  with members:
    - "statelens a reverse engineering solution for making existing dynamic touchscreens accessible"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "ai fairness for people with disabilities point of view"
    - "a reductions approach to fair classification"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-7 (induced by source paper "investigating human machine complementarity for recidivism predictions"):
  with members:
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "inherent trade offs in the fair determination of risk scores"

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)