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: "cost oblivious storage reallocation"
  Abstract: Databases need to allocate and free blocks of storage on disk. Freed blocks introduce holes where no data is stored. Allocation systems attempt to reuse such deallocated regions in order to minimize the footprint on disk. If previously allocated blocks cannot be moved, the problem is called the memory allocation problem, which is known to have a logarithmic overhead in the footprint. #R##N#This paper defines the storage reallocation problem, where previously allocated blocks can be moved, or reallocated, but at some cost. The algorithms presented here are cost oblivious, in that they work for a broad and reasonable class of cost functions, even when they do not know what the cost function is. #R##N#The objective is to minimize the storage footprint, that is, the largest memory address containing an allocated object, while simultaneously minimizing the reallocation costs. This paper gives asymptotically optimal algorithms for storage reallocation, in which the storage footprint is at most (1+epsilon) times optimal, and the reallocation cost is at most (1/epsilon) times the original allocation cost, which is also optimal. The algorithms are cost oblivious as long as the allocation/reallocation cost function is subadditive.

Connected through hyperedge HE-1 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "memory efficient implementation of densenets"
    - "comparative study of deep learning software frameworks"
    - "lifetime based memory management for distributed data processing systems"
    - "database meets deep learning challenges and opportunities"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-2 (induced by source paper "reoptimization via gradual transformations"):
  with members:
    - "fully dynamic maximal independent set with sublinear update time"
    - "new algorithms for steiner tree reoptimization"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "fully dynamic almost maximal matching breaking the polynomial barrier for worst case time bounds"
    - "the shortest augmenting paths for online matchings on trees"
    - "a tight bound for shortest augmenting paths on trees"
    - "fully dynamic maximal matching in constant update time"

Connected through hyperedge HE-3 (induced by source paper "a reallocation algorithm for online split packing of circles"):
  with members:
    - "online circle and sphere packing"
    - "fully dynamic bin packing revisited"
    - "maintaining arrays of contiguous objects"
    - "online square packing"

Connected through hyperedge HE-4 (induced by source paper "an efficient data structure for dynamic two dimensional reconfiguration"):
  with members:
    - "online square into square packing"
    - "reallocation problems in scheduling"
    - "maintaining arrays of contiguous objects"
    - "online square packing"

Connected through hyperedge HE-5 (induced by source paper "dynamic reallocation problems in scheduling"):
  with members:
    - "reallocation problems in scheduling"

Connected through hyperedge HE-6 (induced by source paper "reallocation problems in scheduling"):
  with members:
    - "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)