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: "online balanced repartitioning"
  Abstract: This paper initiates the study of a fundamental online problem called online balanced repartitioning. Unlike the classic graph partitioning problem, our input is an arbitrary sequence of communication requests between nodes, with patterns that may change over time. The objective is to dynamically repartition the $n$ nodes into $\ell$ clusters, each of size $k$. Every communication request needs to be served either locally (cost 0), if the communicating nodes are collocated in the same cluster, or remotely (cost 1), using inter-cluster communication, if they are located in different clusters. The algorithm can also dynamically update the partitioning by migrating nodes between clusters at cost $\alpha$ per node migration. Therefore, we are interested in online algorithms which find a good trade-off between the communication cost and the migration cost, maintaining partitions which minimize the number of inter-cluster communications. #R##N#We consider settings both with and without cluster-size augmentation. For the former, we prove a lower bound which is strictly larger than $k$, which highlights an interesting difference to online paging. Somewhat surprisingly, and unlike online paging, we prove that any deterministic online algorithm has a non-constant competitive ratio of at least $k$, even with augmentation. Our main technical contribution is an $O(k \log{k})$-competitive algorithm for the setting with (constant) augmentation. #R##N#We believe that our model finds interesting applications, e.g., in the context of datacenters, where virtual machines need to be dynamically embedded on a set of (multi-core) servers, and where machines migrations are possible, but costly.

Connected through hyperedge HE-1 (induced by source paper "renets toward statically optimal self adjusting networks"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "toward demand aware networking a theory for self adjusting networks"
    - "demand aware network designs of bounded degree"
    - "high throughput data center topology design"
    - "jellyfish networking data centers randomly"
    - "competitive paging algorithms"

Connected through hyperedge HE-2 (induced by source paper "efficient distributed workload re embedding"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "toward demand aware networking a theory for self adjusting networks"
    - "dynamic balanced graph partitioning"
    - "online companion caching"
    - "competitive paging algorithms"

Connected through hyperedge HE-3 (induced by source paper "self adjusting linear networks"):
  with members:
    - "toward demand aware networking a theory for self adjusting networks"
    - "demand aware network designs of bounded degree"
    - "towards communication aware robust topologies"
    - "locally self adjusting skip graphs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "toward demand aware networking a theory for self adjusting networks"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "virtual network embedding approximations leveraging randomized rounding"
    - "repetita repeatable experiments for performance evaluation of traffic engineering algorithms"
    - "demand aware network designs of bounded degree"
    - "concurrent self adjusting distributed tree networks"

Connected through hyperedge HE-5 (induced by source paper "dynamic balanced graph partitioning"):
  with members:
    - "online network design algorithms via hierarchical decompositions"
    - "competitive paging algorithms"

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)