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: "optimal auctions with correlated bidders are easy"
  Abstract: We consider the problem of designing a revenue-maximizing auction for a single item, when the values of the bidders are drawn from a correlated distribution. We observe that there exists an algorithm that finds the optimal randomized mechanism that runs in time polynomial in the size of the support. We leverage this result to show that in the oracle model introduced by Ronen and Saberi [FOCS'02], there exists a polynomial time truthful in expectation mechanism that provides a $(\frac 3 2+\epsilon)$-approximation to the revenue achievable by an optimal truthful-in-expectation mechanism, and a polynomial time deterministic truthful mechanism that guarantees $\frac 5 3$ approximation to the revenue achievable by an optimal deterministic truthful mechanism. #R##N#We show that the $\frac 5 3$-approximation mechanism provides the same approximation ratio also with respect to the optimal truthful-in-expectation mechanism. This shows that the performance gap between truthful-in-expectation and deterministic mechanisms is relatively small. En route, we solve an open question of Mehta and Vazirani [EC'04]. #R##N#Finally, we extend some of our results to the multi-item case, and show how to compute the optimal truthful-in-expectation mechanisms for bidders with more complex valuations.

Connected through hyperedge HE-1 (induced by source paper "lp based approximation for personalized reserve prices"):
  with members:
    - "prophet secretary through blind strategies"
    - "improved approximations for free order prophets and second price auctions"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "a field guide to personalized reserve prices"
    - "making the most of your samples"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "mechanism design via correlation gap"

Connected through hyperedge HE-2 (induced by source paper "combinatorial auctions with interdependent valuations sos to the rescue"):
  with members:
    - "interdependent values without single crossing"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "approximate revenue maximization in interdependent value settings"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-3 (induced by source paper "from bayesian to crowdsourced bayesian auctions"):
  with members:
    - "bayesian auctions with efficient queries"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-4 (induced by source paper "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "budget feasible mechanisms for experimental design"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "the computational complexity of truthfulness in combinatorial auctions"

Connected through hyperedge HE-5 (induced by source paper "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"):
  with members:
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-6 (induced by source paper "sequential posted price mechanisms with correlated valuations"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "online contention resolution schemes"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "approximate revenue maximization in interdependent value settings"
    - "combinatorial auctions via posted prices"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"

Connected through hyperedge HE-7 (induced by source paper "on the efficiency of all pay mechanisms"):
  with members:
    - "the price of anarchy in large games"
    - "welfare guarantees for proportional allocations"
    - "tight bounds for the price of anarchy of simultaneous first price auctions"
    - "on the inefficiency of standard multi unit auctions"
    - "composable and efficient mechanisms"
    - "simultaneous auctions are almost efficient"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-8 (induced by source paper "public projects boolean functions and the borders of border s theorem"):
  with members:
    - "analysis of boolean functions"
    - "the complexity of optimal multidimensional pricing"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "an algorithmic characterization of multi dimensional mechanisms"

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)