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: "tight bounds for online vector scheduling"
  Abstract: Modern data centers face a key challenge of effectively serving user requests that arrive online. Such requests are inherently multi-dimensional and characterized by demand vectors over multiple resources such as processor cycles, storage space, and network bandwidth. Typically, different resources require different objectives to be optimized, and $L_r$ norms of loads are among the most popular objectives considered. To address these problems, we consider the online vector scheduling problem in this paper. Introduced by Chekuri and Khanna (SIAM J of Comp. 2006), vector scheduling is a generalization of classical load balancing, where every job has a vector load instead of a scalar load. In this paper, we resolve the online complexity of the vector scheduling problem and its important generalizations. Our main results are: #R##N#-For identical machines, we show that the optimal competitive ratio is $\Theta(\log d / \log \log d)$ by giving an online lower bound and an algorithm with an asymptotically matching competitive ratio. The lower bound is technically challenging, and is obtained via an online lower bound for the minimum mono-chromatic clique problem using a novel online coloring game and randomized coding scheme. #R##N#-For unrelated machines, we show that the optimal competitive ratio is $\Theta(\log m + \log d)$ by giving an online lower bound that matches a previously known upper bound. Unlike identical machines, however, extending these results, particularly the upper bound, to general $L_r$ norms requires new ideas. In particular, we use a carefully constructed potential function that balances the individual $L_r$ objectives with the overall (convexified) min-max objective to guide the online algorithm and track the changes in potential to bound the competitive ratio.

Connected through hyperedge HE-1 (induced by source paper "streaming algorithms for bin packing and vector scheduling"):
  with members:
    - "tight lower bound for comparison based quantile summaries"
    - "a new and improved algorithm for online bin packing"
    - "fully dynamic bin packing with limited repacking"
    - "optimal quantile approximation in streams"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "a logarithmic additive integrality gap for bin packing"
    - "polynomiality for bin packing with a constant number of item types"
    - "on the optimality of approximation schemes for the classical scheduling problem"

Connected through hyperedge HE-2 (induced by source paper "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"):
  with members:
    - "edos edge assisted offloading system for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "online primal dual algorithms with configuration linear programs"):
  with members:
    - "online covering with sum of ell_q norm objectives"
    - "online submodular maximization with preemption"
    - "online contention resolution schemes"
    - "welfare maximization with production costs a primal dual approach"
    - "selfishmigrate a scalable algorithm for non clairvoyantly scheduling heterogeneous processors"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "online primal dual for non linear optimization with applications to speed scaling"
    - "welfare and profit maximization with production costs"

Connected through hyperedge HE-4 (induced by source paper "makespan minimization via posted prices"):
  with members:
    - "the invisible hand of dynamic market pricing"
    - "pricing online decisions beyond auctions"
    - "combinatorial auctions via posted prices"
    - "combinatorial walrasian equilibrium"
    - "the power of randomness in bayesian optimal mechanism design"
    - "algorithmic pricing via virtual valuations"

Connected through hyperedge HE-5 (induced by source paper "online and random order load balancing simultaneously"):
  with members:
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "fast algorithms for online stochastic convex programming"
    - "online packing and covering framework with convex objectives"
    - "online linear optimization via smoothing"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-6 (induced by source paper "do the hard stuff first scheduling dependent computations in data analytics clusters"):
  with members:
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

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)