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: "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
  Abstract: We design an expected polynomial-time, truthful-in-expectation, (1-1/e)-approximation mechanism for welfare maximization in a fundamental class of combinatorial auctions. Our results apply to bidders with valuations that are m matroid rank sums (MRS), which encompass most concrete examples of submodular functions studied in this context, including coverage functions, matroid weighted-rank functions, and convex combinations thereof. Our approximation factor is the best possible, even for known and explicitly given coverage valuations, assuming P != NP. Ours is the first truthful-in-expectation and polynomial-time mechanism to achieve a constant-factor approximation for an NP-hard welfare maximization problem in combinatorial auctions with heterogeneous goods and restricted valuations. #R##N#Our mechanism is an instantiation of a new framework for designing approximation mechanisms based on randomized rounding algorithms. A typical such algorithm first optimizes over a fractional relaxation of the original problem, and then randomly rounds the fractional solution to an integral one. With rare exceptions, such algorithms cannot be converted into truthful mechanisms. The high-level idea of our mechanism design framework is to optimize directly over the (random) output of the rounding algorithm, rather than over the input to the rounding algorithm. This approach leads to truthful-in-expectation mechanisms, and these mechanisms can be implemented efficiently when the corresponding objective function is concave. For bidders with MRS valuations, we give a novel randomized rounding algorithm that leads to both a concave objective function and a (1-1/e)-approximation of the optimal welfare.

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 "a bridge between liquid and social welfare in combinatorial auctions with submodular bidders"):
  with members:
    - "a bayesian clearing mechanism for combinatorial auctions"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "bayesian budget feasibility with posted pricing"
    - "combinatorial auctions via posted prices"
    - "budget feasible mechanism design from prior free to bayesian"
    - "on the approximability of budget feasible mechanisms"

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 "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 "computational efficiency requires simple taxation"):
  with members:
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "inapproximability of truthful mechanisms via generalizations of the vc dimension"
    - "economic efficiency requires interaction"
    - "multi parameter mechanisms with implicit payment computation"
    - "the menu size complexity of auctions"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "single call mechanisms"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "a note on relaxation and rounding in algorithmic mechanism design"):
  with members:
    - "generalized assignment problem truthful mechanism design without money"
    - "a truthful in expectation mechanism for the generalized assignment problem"
    - "truthful assignment without money"
    - "truthfulness via proxies"
    - "on the power of randomization in algorithmic mechanism design"

Connected through hyperedge HE-8 (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"

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)