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: "on topological watersheds"
  Abstract: In this paper, we investigate topological watersheds (Couprie and Bertrand, 1997). One of our main results is a necessary and sufficient condition for a map G to be a watershed of a map F, this condition is based on a notion of extension. A consequence of the theorem is that there exists a (greedy) polynomial time algorithm to decide whether a map G is a watershed of a map F or not. We introduce a notion of "separation between two points" of an image which leads to a second necessary and sufficient condition. We also show that, given an arbitrary total order on the minima of a map, it is possible to define a notion of "degree of separation of a minimum" relative to this order. This leads to a third necessary and sufficient condition for a map G to be a watershed of a map F. At last we derive, from our framework, a new definition for the dynamics of a minimum.

Connected through hyperedge HE-1 (induced by source paper "a graph based mathematical morphology reader"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "a contrario selection of optimal partitions for image segmentation"
    - "morphological filtering in shape spaces applications using tree based image representations"
    - "on the equivalence between hierarchical segmentations and ultrametric watersheds"
    - "connected operators a review of region based morphological image processing techniques"

Connected through hyperedge HE-2 (induced by source paper "real time topological image smoothing on shared memory parallel machines"):
  with members:
    - "parallel image thinning through topological operators on shared memory parallel machines"

Connected through hyperedge HE-3 (induced by source paper "on morphological hierarchical representations for image processing and spatial data clustering"):
  with members:
    - "on the equivalence between hierarchical segmentations and ultrametric watersheds"
    - "writing reusable digital topology algorithms in a generic image processing framework"
    - "connected operators a review of region based morphological image processing techniques"

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)