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: "limitations of randomized mechanisms for combinatorial auctions"
  Abstract: Recently, a randomized mechanism has been discovered [Dughmi, Roughgarden and Yan; STOC'11] for combinatorial auctions that is truthful in expectation and guarantees a (1-1/e)-approximation to the optimal social welfare when players have coverage valuations. This approximation ratio is the best possible even for non-truthful algorithms, assuming $P \neq NP$. Given the recent sequence of negative results for combinatorial auctions under more restrictive notions of incentive compatibility, this development raises a natural question: Are truthful-in-expectation mechanisms compatible with polynomial-time approximation in a way that deterministic or universally truthful mechanisms are not? In particular, can polynomial-time truthful-in-expectation mechanisms guarantee a near-optimal approximation ratio for more general variants of combinatorial auctions? #R##N#We prove that this is not the case. Specifically, the result of Dughmi, Roughgarden and Yan cannot be extended to combinatorial auctions with submodular valuations in the value oracle model. (Absent strategic considerations, a (1-1/e)-approximation is still achievable in this setting.) More precisely, we prove that there is a constant \gamma>0 such that there is no randomized mechanism that is truthful-in-expectation--- or even approximately truthful-in-expectation --- and guarantees an m^{-\gamma}-approximation to the optimal social welfare for combinatorial auctions with submodular valuations in the value oracle model. We also prove an analogous result for the flexible combinatorial public projects (CPP) problem. Both our results present an unexpected separation between coverage functions and submodular functions, which does not occur for these problems without strategic considerations.

Connected through hyperedge HE-1 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders"):
  with members:
    - "complement free couples must communicate a hardness result for two player combinatorial auctions"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "prophet secretary for combinatorial auctions and matroids"
    - "on simultaneous two player combinatorial auctions"
    - "combinatorial auctions do need modest interaction"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "computational efficiency requires simple taxation"
    - "online matroid intersection beating half for random arrival"

Connected through hyperedge HE-2 (induced by source paper "tight approximation bounds for maximum multi coverage"):
  with members:
    - "algorithmic aspects of optimal channel coding"
    - "learning coverage functions and private release of marginals"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-3 (induced by source paper "on simultaneous two player combinatorial auctions"):
  with members:
    - "simple mechanisms for agents with complements"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "welfare maximization with limited interaction"
    - "interpolating between truthful and non truthful mechanisms for combinatorial auctions"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "inapproximability of truthful mechanisms via generalizations of the vc dimension"
    - "combinatorial auctions via posted prices"
    - "the computational complexity of truthfulness in combinatorial auctions"

Connected through hyperedge HE-4 (induced by source paper "bernoulli factories and black box reductions in mechanism design"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "non optimal mechanism design"
    - "fast algorithms for online stochastic convex programming"
    - "a near optimal dynamic learning algorithm for online matching problems with concave returns"
    - "bandits with knapsacks"
    - "multi parameter mechanisms with implicit payment computation"
    - "primal beats dual on online packing lps in the random order model"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"

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"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "optimal auctions with correlated bidders are easy"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-6 (induced by source paper "the limitations of optimization from samples"):
  with members:
    - "introduction to online convex optimization"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "submodular optimization under noise"
    - "robust influence maximization"
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-7 (induced by source paper "learning in auctions regret is hard envy is easy"):
  with members:
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "interpolating between truthful and non truthful mechanisms for combinatorial auctions"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "combinatorial auctions via posted prices"
    - "valuation compressions in vcg based combinatorial auctions"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "locally adaptive optimization adaptive seeding for monotone submodular functions"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "maximizing acceptance probability for active friending in on line social networks"
    - "maximizing social influence in nearly optimal time"
    - "symmetry and approximability of submodular maximization problems"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "inferring networks of diffusion and influence"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)