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: "parallel minimum cuts in near linear work and low depth"
  Abstract: We present the first near-linear work and poly-logritharithmic depth algorithm for computing a minimum cut in a graph, while previous parallel algorithms with poly-logarithmic depth required at least quadratic work in the number of vertices. In a graph with n vertices and m edges, our algorithm computes the correct result with high probability in $O(m log^4 n)$ work and $O(log^3 n)$ depth. This result is obtained by parallelizing a data structure that aggregates weights along paths in a tree and by exploiting the connection between minimum cuts and approximate maximum packings of spanning trees. In addition, our algorithm improves upon bounds on the number of cache misses incurred to compute a minimum cut.

Connected through hyperedge HE-1 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model"):
  with members:
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "parallel batch dynamic graph connectivity"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "faster fully dynamic minimum spanning forest"
    - "single pass sparsification in the streaming model with edge deletions"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-2 (induced by source paper "faster algorithms for edge connectivity via random 2 out contractions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "distributed edge connectivity in sublinear time"
    - "random k out subgraph leaves only o n k inter component edges"
    - "exponentially faster massively parallel maximal matching"
    - "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-3 (induced by source paper "a simple algorithm for minimum cuts in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "practical minimum cut algorithms"
    - "local flow partitioning for faster edge connectivity"
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "deterministic edge connectivity in near linear time"
    - "distributed minimum cut approximation"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-4 (induced by source paper "minimum cut in o m log 2 n time"):
  with members:
    - "a simple algorithm for minimum cuts in near linear time"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "local flow partitioning for faster edge connectivity"
    - "computing maximum flow with augmenting electrical flows"
    - "deterministic edge connectivity in near linear time"
    - "maintaining information in fully dynamic trees with top trees"
    - "randomized rounding without solving the linear program"
    - "minimum cuts in near linear time"

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.DC(Distributed, Parallel, and Cluster Computing)