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: "practical massively parallel sorting"
  Abstract: Previous parallel sorting algorithms do not scale to the largest available machines, since they either have prohibitive communication volume or prohibitive critical path length. We describe algorithms that are a viable compromise and overcome this gap both in theory and practice. The algorithms are multi-level generalizations of the known algorithms sample sort and multiway mergesort. In particular our sample sort variant turns out to be very scalable. Some tools we develop may be of independent interest -- a simple, practical, and flexible sorting algorithm for small inputs working in logarithmic time, a near linear time optimal algorithm for solving a constrained bin packing problem, and an algorithm for data delivery, that guarantees a small number of message startups on each processor.

Connected through hyperedge HE-1 (induced by source paper "communication efficient string sorting"):
  with members:
    - "scalable string and suffix sorting algorithms techniques and tools"
    - "robust massively parallel sorting"
    - "engineering parallel string sorting"
    - "parallel string sample sort"
    - "scalable distributed memory external sorting"

Connected through hyperedge HE-2 (induced by source paper "connected components on a pram in log diameter time"):
  with members:
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "robust massively parallel sorting"
    - "communication steps for parallel query processing"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-3 (induced by source paper "communication efficient weighted reservoir sampling"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "parallel weighted random sampling"
    - "weighted sampling without replacement from data streams"
    - "fast parallel operations on search trees"
    - "weighted random sampling over data streams"

Connected through hyperedge HE-4 (induced by source paper "histogram sort with sampling"):
  with members:
    - "sorting with asymmetric read and write costs"

Connected through hyperedge HE-5 (induced by source paper "efficient parallel random sampling vectorized cache efficient and online"):
  with members:
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-6 (induced by source paper "lightweight mpi communicators with applications to perfectly balanced quicksort"):
  with members:
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "robust massively parallel sorting"

Connected through hyperedge HE-7 (induced by source paper "robust massively parallel sorting"):
  with members:
    - "resource oblivious sorting on multicores"

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)