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: "revenue maximization via hiding item attributes"
  Abstract: We study probabilistic single-item second-price auctions where the item is characterized by a set of attributes. The auctioneer knows the actual instantiation of all the attributes, but he may choose to reveal only a subset of these attributes to the bidders. Our model is an abstraction of the following Ad auction scenario. The website (auctioneer) knows the demographic information of its impressions, and this information is in terms of a list of attributes (e.g., age, gender, country of location). The website may hide certain attributes from its advertisers (bidders) in order to create thicker market, which may lead to higher revenue. We study how to hide attributes in an optimal way. We show that it is NP-hard to solve for the optimal attribute hiding scheme. We then derive a polynomial-time solvable upper bound on the optimal revenue. Finally, we propose two heuristic-based attribute hiding schemes. Experiments show that revenue achieved by these schemes is close to the upper bound.

Connected through hyperedge HE-1 (induced by source paper "access to population level signaling as a source of inequality"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness incentives for myopic agents"
    - "fairness at equilibrium in the labor market"
    - "algorithmic persuasion with no externalities"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-2 (induced by source paper "persuasion with limited communication"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"
    - "auctions with severely bounded communication"

Connected through hyperedge HE-3 (induced by source paper "algorithmic bayesian persuasion"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "public projects boolean functions and the borders of border s theorem"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "constrained signaling in auction design"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "send mixed signals earn more work less"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"

Connected through hyperedge HE-4 (induced by source paper "near optimal hardness results for signaling in bayesian games"):
  with members:
    - "eth hardness for signaling in symmetric zero sum games"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-5 (induced by source paper "hardness results for signaling in bayesian zero sum and network routing games"):
  with members:
    - "eth hardness for signaling in symmetric zero sum games"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-6 (induced by source paper "mixture selection mechanism design and signaling"):
  with members:
    - "sampling and representation complexity of revenue maximization"
    - "analysis of boolean functions"
    - "on the hardness of signaling"
    - "a polynomial time approximation scheme for fault tolerant distributed storage"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-7 (induced by source paper "signaling in quasipolynomial time"):
  with members:
    - "on the hardness of signaling"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-8 (induced by source paper "on the hardness of signaling"):
  with members:
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"

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)