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: "efficient search for diverse coherent explanations"
  Abstract: This paper proposes new search algorithms for counterfactual explanations based upon mixed integer programming. We are concerned with complex data in which variables may take any value from a contiguous range or an additional set of discrete states. We propose a novel set of constraints that we refer to as a "mixed polytope" and show how this can be used with an integer programming solver to efficiently find coherent counterfactual explanations i.e. solutions that are guaranteed to map back onto the underlying data structure, while avoiding the need for brute-force enumeration. We also look at the problem of diverse explanations and show how these can be generated within our framework.

Connected through hyperedge HE-1 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-3 (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"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "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-4 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-5 (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"
    - "model agnostic counterfactual explanations for consequential decisions"

Connected through hyperedge HE-6 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "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-7 (induced by source paper "explaining machine learning classifiers through diverse counterfactual explanations"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding deep image representations by inverting them"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "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"

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)