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: "local algorithms in weakly coloured graphs"
  Abstract: A local algorithm is a distributed algorithm that completes after a constant number of synchronous communication rounds. We present local approximation algorithms for the minimum dominating set problem and the maximum matching problem in 2-coloured and weakly 2-coloured graphs. In a weakly 2-coloured graph, both problems admit a local algorithm with the approximation factor ( + 1) =2, where is the maximum degree of the graph. We also give a matching lower bound proving that there is no local algorithm with a better approximation factor for either of these problems. Furthermore, we show that the stronger assumption of a 2-colouring does not help in the case of the dominating set problem, but there is a local approximation scheme for the maximum matching problem in 2-coloured graphs.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "approximation ratios of graph neural networks for combinatorial problems"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "how powerful are graph neural networks"
    - "struc2vec learning node representations from structural identity"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "semi supervised classification with graph convolutional networks"
    - "weak models of distributed computing with connections to modal logic"

Connected through hyperedge HE-3 (induced by source paper "weak models of distributed computing with connections to modal logic"):
  with members:
    - "lower bounds for local approximation"
    - "beeping a maximal independent set"
    - "stone age distributed computing"
    - "almost stable matchings by truncating the gale shapley algorithm"
    - "deploying wireless networks with beeps"
    - "an optimal local approximation algorithm for max min linear programs"
    - "a simple local 3 approximation algorithm for vertex cover"
    - "local approximation algorithms for a class of 0 1 max min linear programs"

Connected through hyperedge HE-4 (induced by source paper "no sublogarithmic time approximation scheme for bipartite vertex cover"):
  with members:
    - "lower bounds for local approximation"
    - "local computation lower and upper bounds"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for local approximation"):
  with members:
    - "weak models of distributed computing with connections to modal logic"
    - "distributed maximal matching greedy is optimal"
    - "on the impact of identifiers on local decision"
    - "local computation lower and upper bounds"

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)