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: "mechanism design for social good"
  Abstract: Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an out-sized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of interdisciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities. To realize the opportunities in these domains, we need to correctly set objectives and reason about human behavior and actions. Doing so requires a deep grounding in the field of interest and collaboration with domain experts who understand the societal implications and feasibility of proposed solutions. These insights can play an instrumental role in proposing algorithmically-informed policies. #R##N#In this article, we describe the Mechanism Design for Social Good (MD4SG) research agenda, which involves using insights from algorithms, optimization, and mechanism design to improve access to opportunity. The MD4SG research community takes an interdisciplinary, multi-stakeholder approach to improve societal welfare. We discuss three exciting research avenues within MD4SG related to improving access to opportunity in the developing world, labor markets and discrimination, and housing. For each of these, we showcase ongoing work, underline new directions, and discuss potential for implementing existing work in practice.

Connected through hyperedge HE-1 (induced by source paper "roles for computing in social change"):
  with members:
    - "what is the point of fairness disability ai and the complexity of justice"
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "mitigating bias in algorithmic hiring evaluating claims and practices"
    - "model cards for model reporting"
    - "on the legal compatibility of fairness definitions"
    - "problem formulation and fairness"
    - "data science as political action grounding data science in a politics of justice"
    - "using search queries to understand health information needs in africa"

Connected through hyperedge HE-2 (induced by source paper "algorithmic and economic perspectives on fairness"):
  with members:
    - "fair classification and social welfare"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness incentives for myopic agents"
    - "algorithmic decision making and the cost of fairness"
    - "runaway feedback loops in predictive policing"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "censoring representations with an adversary"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-3 (induced by source paper "acceptable planning influencing individual behavior to reduce transportation energy expenditure of a city"):
  with members:
    - "artificial intelligence for social good"
    - "challenges of human aware ai systems"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"

Connected through hyperedge HE-4 (induced by source paper "designing an ai health coach and studying its utility in promoting regular aerobic exercise"):
  with members:
    - "acceptable planning influencing individual behavior to reduce transportation energy expenditure of a city"
    - "challenges of human aware ai systems"

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.GT(Computer Science and Game Theory)