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: "a distributed mincut maxflow algorithm combining path augmentation and push relabel"
  Abstract: We develop a novel distributed algorithm for the minimum cut problem. We primarily aim at solving large sparse problems. Assuming vertices of the graph are partitioned into several regions, the algorithm performs path augmentations inside the regions and updates of the push-relabel style between the regions. The interaction between regions is considered expensive (regions are loaded into the memory one-by-one or located on separate machines in a network). The algorithm works in sweeps - passes over all regions. Let $B$ be the set of vertices incident to inter-region edges of the graph. We present a sequential and parallel versions of the algorithm which terminate in at most $2|B|^2+1$ sweeps. The competing algorithm by Delong and Boykov uses push-relabel updates inside regions. In the case of a fixed partition we prove that this algorithm has a tight $O(n^2)$ bound on the number of sweeps, where $n$ is the number of vertices. We tested sequential versions of the algorithms on instances of maxflow problems in computer vision. Experimentally, the number of sweeps required by the new algorithm is much lower than for the Delong and Boykov's variant. Large problems (up to $10^8$ vertices and $6\cdot 10^8$ edges) are solved using under 1GB of memory in about 10 sweeps.

Connected through hyperedge HE-1 (induced by source paper "fast decomposable submodular function minimization using constrained total variation"):
  with members:
    - "decomposable submodular function minimization discrete and continuous"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "submodular functions from discrete to continous domains"
    - "convex optimization for parallel energy minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "provable submodular minimization using wolfe s algorithm"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-2 (induced by source paper "memory efficient max flow for multi label submodular mrfs"):
  with members:
    - "iteratively reweighted graph cut for multi label mrfs with non convex priors"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "opengm a c library for discrete graphical models"

Connected through hyperedge HE-3 (induced by source paper "reflection methods for user friendly submodular optimization"):
  with members:
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "minimizing a sum of submodular functions"

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)