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: "on the hardness of signaling"
  Abstract: There has been a recent surge of interest in the role of information in strategic interactions. Much of this work seeks to understand how the realized equilibrium of a game is influenced by uncertainty in the environment and the information available to players in the game. Lurking beneath this literature is a fundamental, yet largely unexplored, algorithmic question: how should a "market maker" who is privy to additional information, and equipped with a specified objective, inform the players in the game? This is an informational analogue of the mechanism design question, and views the information structure of a game as a mathematical object to be designed, rather than an exogenous variable. #R##N#We initiate a complexity-theoretic examination of the design of optimal information structures in general Bayesian games, a task often referred to as signaling. We focus on one of the simplest instantiations of the signaling question: Bayesian zero-sum games, and a principal who must choose an information structure maximizing the equilibrium payoff of one of the players. In this setting, we show that optimal signaling is computationally intractable, and in some cases hard to approximate, assuming that it is hard to recover a planted clique from an Erdos-Renyi random graph. This is despite the fact that equilibria in these games are computable in polynomial time, and therefore suggests that the hardness of optimal signaling is a distinct phenomenon from the hardness of equilibrium computation. Necessitated by the non-local nature of information structures, en-route to our results we prove an "amplification lemma" for the planted clique problem which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "the complexity of contracts"):
  with members:
    - "delegated search approximates efficient search"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bernoulli factories and black box reductions in mechanism design"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"

Connected through hyperedge HE-2 (induced by source paper "a duality based unified approach to bayesian mechanism design"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "mechanism design for subadditive agents via an ex ante relaxation"

Connected through hyperedge HE-3 (induced by source paper "reducibility and computational lower bounds for problems with planted sparse structure"):
  with members:
    - "sherali adams integrality gaps matching the log density threshold"
    - "on finding dense common subgraphs"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "sum of squares lower bounds for refuting any csp"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"

Connected through hyperedge HE-4 (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-5 (induced by source paper "information signal design for incentivizing team formation"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"
    - "hardness results for signaling in bayesian zero sum and network routing games"

Connected through hyperedge HE-6 (induced by source paper "algorithmic persuasion with no externalities"):
  with members:
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "understanding incentives mechanism design becomes algorithm design"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"

Connected through hyperedge HE-7 (induced by source paper "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "sum of squares lower bounds for planted clique"
    - "noisy sorting without resampling"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-8 (induced by source paper "persuasion with limited communication"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "constrained signaling in auction design"
    - "revenue maximization via hiding item attributes"
    - "send mixed signals earn more work less"
    - "auctions with severely bounded communication"

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)