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: "fully dynamic bin packing with limited repacking"
  Abstract: We study the classic Bin Packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio \emph{while repacking items sparingly} between updates. Formally, each item $i$ has a \emph{movement cost} $c_i\geq 0$, and we want to use $\alpha \cdot OPT$ bins and incur a movement cost $\gamma\cdot c_i$, either in the worst case, or in an amortized sense, for $\alpha, \gamma$ as small as possible. We call $\gamma$ the \emph{recourse} of the algorithm. This is motivated by cloud storage applications, where fully-dynamic Bin Packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure.

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"
    - "optimal quantile approximation in streams"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "a logarithmic additive integrality gap for bin packing"
    - "tight bounds for online vector scheduling"
    - "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 "permutation strikes back the power of recourse in online metric matching"):
  with members:
    - "stochastic online metric matching"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "maintaining perfect matchings at low cost"
    - "a match in time saves nine deterministic online matching with delays"
    - "a collection of lower bounds for online matching on the line"
    - "online and dynamic algorithms for set cover"
    - "online matching haste makes waste"
    - "new algorithms better bounds and a novel model for online stochastic matching"

Connected through hyperedge HE-3 (induced by source paper "online strip packing with polynomial migration"):
  with members:
    - "fully dynamic bin packing revisited"

Connected through hyperedge HE-4 (induced by source paper "fully dynamic bin packing revisited"):
  with members:
    - "a new and improved algorithm for online bin packing"
    - "online strip packing with polynomial migration"
    - "a tight approximation for fully dynamic bin packing without bundling"
    - "beating the harmonic lower bound for online bin packing"
    - "a faster fptas for the unbounded knapsack problem"
    - "a robust afptas for online bin packing with polynomial migration"

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)