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: "model agnostic counterfactual explanations for consequential decisions"
  Abstract: Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a prediction was output, but also how to act to obtain a desired outcome. To this end, several works have proposed methods to generate counterfactual explanations. However, they are often restricted to a particular subset of models (e.g., decision trees or linear models), and cannot directly handle the mixed (numerical and nominal) nature of the features describing each individual. In this paper, we propose a model-agnostic algorithm to generate counterfactual explanations that builds on the standard theory and tools from formal verification. Specifically, our algorithm solves a sequence of satisfiability problems, where a wide variety of predictive models and distances in mixed feature spaces, as well as natural notions of plausibility and diversity, are represented as logic formulas. Our experiments on real-world data demonstrate that our approach can flexibly handle widely deployed predictive models, while providing meaningfully closer counterfactuals than existing approaches.

Connected through hyperedge HE-1 (induced by source paper "decisions counterfactual explanations and strategic behavior"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "optimal decision making under strategic behavior"
    - "fair decisions despite imperfect predictions"
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"

Connected through hyperedge HE-2 (induced by source paper "algorithmic recourse from counterfactual explanations to interventions"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "preserving causal constraints in counterfactual explanations for machine learning classifiers"
    - "face feasible and actionable counterfactual explanations"
    - "efficient search for diverse coherent explanations"

Connected through hyperedge HE-3 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "delayed impact of fair machine learning"
    - "interpretable credit application predictions with counterfactual explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-4 (induced by source paper "extracting incentives from black box decisions"):
  with members:
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "synthesizing action sequences for modifying model decisions"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-5 (induced by source paper "actionable interpretability through optimizable counterfactual explanations for tree ensembles"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "efficient search for diverse coherent explanations"
    - "model agnostic contrastive explanations for structured data"
    - "generating counterfactual and contrastive explanations using shap"
    - "interpretable credit application predictions with counterfactual explanations"
    - "local rule based explanations of black box decision systems"
    - "deep neural decision trees"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

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)