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: "oblivious algorithms for the maximum directed cut problem"
  Abstract: This paper introduces a special family of randomized algorithms for Max DICUT that we call oblivious algorithms. Let the bias of a vertex be the ratio between the total weight of its outgoing edges and the total weight of all its edges. An oblivious algorithm selects at random in which side of the cut to place a vertex v, with probability that only depends on the bias of v, independently of other vertices. The reader may observe that the algorithm that ignores the bias and chooses each side with probability 1/2 has an approximation ratio of 1/4, whereas no oblivious algorithm can have an approximation ratio better than 1/2 (with an even directed cycle serving as a negative example). We attempt to characterize the best approximation ratio achievable by oblivious algorithms, and present results that are nearly tight. The paper also discusses natural extensions of the notion of oblivious algorithms, and extensions to the more general problem of Max 2-AND.

Connected through hyperedge HE-1 (induced by source paper "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship"):
  with members:
    - "size versus truthfulness in the house allocation problem"
    - "egalitarianism of random assignment mechanisms"
    - "near optimal asymmetric binary matrix partitions"
    - "an axiomatic approach to characterizing and relaxing strategyproofness of one sided matching mechanisms"
    - "social welfare in one sided matchings random priority and beyond"
    - "welfare maximization and truthfulness in mechanism design with ordinal preferences"
    - "truthful approximations to range voting"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-2 (induced by source paper "online submodular maximization with preemption"):
  with members:
    - "submodular maximization meets streaming matchings matroids and more"
    - "improved bounds for online preemptive matching"
    - "near optimal nonmyopic value of information in graphical models"
    - "symmetry and approximability of submodular maximization problems"
    - "buyback problem approximate matroid intersection with cancellation costs"
    - "constrained non monotone submodular maximization offline and secretary algorithms"
    - "submodular maximization by simulated annealing"
    - "randomized online algorithms for the buyback problem"

Connected through hyperedge HE-3 (induced by source paper "near optimal asymmetric binary matrix partitions"):
  with members:
    - "send mixed signals earn more work less"
    - "combinatorial auctions with decreasing marginal utilities"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-4 (induced by source paper "bounds on double sided myopic algorithms for unconstrained non monotone submodular maximization"):
  with members:
    - "on some recent max sat approximation algorithms"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular maximization by simulated annealing"

Connected through hyperedge HE-5 (induced by source paper "a unifying tool for bounding the quality of non cooperative solutions in weighted congestion games"):
  with members:
    - "on the robustness of the approximate price of anarchy in generalized congestion games"
    - "on linear congestion games with altruistic social context"
    - "on the performance of approximate equilibria in congestion games"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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.DS(Data Structures and Algorithms)