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: "efficient algorithms with asymmetric read and write costs"
  Abstract: In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fundamentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We consider both the case in which all but $O(1)$ memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the $(M,\omega)$-ARAM, in which there is a small (symmetric) memory of size $M$ and a large unbounded (asymmetric) memory, both random access, and where reading from the large memory has unit cost, but writing has cost $\omega\gg 1$. #R##N#For FFT and sorting networks we show a lower bound cost of $\Omega(\omega n\log_{\omega M} n)$, which indicates that it is not possible to achieve asymptotic improvements with cheaper reads when $\omega$ is bounded by a polynomial in $M$. Also, there is an asymptotic gap (of $\min(\omega,\log n)/\log(\omega M)$) between the cost of sorting networks and comparison sorting in the model. This contrasts with the RAM, and most other models. We also show a lower bound for computations on an $n\times n$ diamond DAG of $\Omega(\omega n^2/M)$ cost, which indicates no asymptotic improvement is achievable with fast reads. However, we show that for the edit distance problem (and related problems), which would seem to be a diamond DAG, there exists an algorithm with only $O(\omega n^2/(M\min(\omega^{1/3},M^{1/2})))$ cost. To achieve this we make use of a "path sketch" technique that is forbidden in a strict DAG computation. Finally, we show several interesting upper bounds for shortest path problems, minimum spanning trees, and other problems. A common theme in many of the upper bounds is to have redundant computation to tradeoff between reads and writes.

Connected through hyperedge HE-1 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams"):
  with members:
    - "pangolin a fault tolerant persistent memory programming library"
    - "tracking in order to recover recoverable lock free data structures"
    - "write optimized and consistent rdma based nvm systems"
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "persistent memory i o primitives"
    - "persistent buffer management with optimistic consistency"
    - "basic performance measurements of the intel optane dc persistent memory module"

Connected through hyperedge HE-2 (induced by source paper "optimal parallel algorithms in the binary forking model"):
  with members:
    - "parallel finger search structures"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "parallel working set search structures"
    - "parallelism in randomized incremental algorithms"
    - "pam parallel augmented maps"
    - "resource oblivious sorting on multicores"
    - "parallel ordered sets using join"

Connected through hyperedge HE-3 (induced by source paper "improved parallel cache oblivious algorithms for dynamic programming and linear algebra"):
  with members:
    - "persistent memory i o primitives"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "algorithmic building blocks for asymmetric memories"
    - "the parallel persistent memory model"
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-4 (induced by source paper "parallel write efficient algorithms and data structures for computational geometry"):
  with members:
    - "algorithmic building blocks for asymmetric memories"
    - "parallelism in randomized incremental algorithms"
    - "pam parallel augmented maps"
    - "sorting with asymmetric read and write costs"
    - "fast parallel operations on search trees"
    - "wear minimization for cuckoo hashing how not to throw a lot of eggs into one basket"

Connected through hyperedge HE-5 (induced by source paper "the parallel persistent memory model"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "sorting with asymmetric read and write costs"
    - "lock free concurrent data structures"

Connected through hyperedge HE-6 (induced by source paper "a lower bound technique for communication in bsp"):
  with members:
    - "resource oblivious sorting on multicores"
    - "communication lower bounds for distributed memory computations"
    - "network oblivious algorithms"
    - "communication lower bounds for distributed memory computations"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-7 (induced by source paper "implicit decomposition for write efficient connectivity algorithms"):
  with members:
    - "sorting with asymmetric read and write costs"
    - "wear minimization for cuckoo hashing how not to throw a lot of eggs into one basket"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-8 (induced by source paper "parallel wavelet tree construction"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "parallel local graph clustering"
    - "sorting with asymmetric read and write costs"
    - "wavelet trees meet suffix trees"
    - "counting triangles in massive graphs with mapreduce"
    - "parallel graph decompositions using random shifts"
    - "skew strikes back new developments in the theory of join algorithms"
    - "parallel triangle counting in massive streaming graphs"

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)