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: "deterministic subgraph detection in broadcast congest"
  Abstract: We present simple deterministic algorithms for subgraph finding and enumeration in the broadcast CONGEST model of distributed computation: #R##N#-- For any constant $k$, detecting $k$-paths and trees on $k$ nodes can be done in constantly many rounds, and $k$-cycles in $O(n)$ rounds. #R##N#-- On $d$-degenerate graphs, cliques and $4$-cycles can be enumerated in $O(d + \log n)$ rounds, and $5$-cycles in $O(d^2 + \log n)$ rounds. #R##N#In many cases, these bounds are tight up to logarithmic factors. Moreover, we show that the algorithms for $d$-degenerate graphs can be improved to optimal complexity $O(d/\log n)$ and $O(d^2/\log n)$, respectively, in the supported CONGEST model, which can be seen as an intermediate model between CONGEST and the congested clique.

Connected through hyperedge HE-1 (induced by source paper "distributed testing of graph isomorphism in the congest model"):
  with members:
    - "distributed detection of cliques in dynamic networks"
    - "property testing of planarity in the congest model"
    - "distributed triangle detection via expander decomposition"
    - "detecting cliques in congest networks"
    - "distributed subgraph detection"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "distributed detection of cycles"
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"

Connected through hyperedge HE-2 (induced by source paper "what graph neural networks cannot learn depth vs width"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mincut pooling in graph neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"

Connected through hyperedge HE-3 (induced by source paper "broadcast congested clique planted cliques and pseudorandom generators"):
  with members:
    - "congested clique algorithms for graph spanners"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "msf and connectivity in limited variants of the congested clique"
    - "a time hierarchy theorem for the local model"
    - "on derandomizing local distributed algorithms"
    - "towards a complexity theory for the congested clique"
    - "deterministic graph connectivity in the broadcast congested clique"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"

Connected through hyperedge HE-4 (induced by source paper "distributed detection of cliques in dynamic networks"):
  with members:
    - "fast and simple deterministic algorithms for highly dynamic networks"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "local distributed algorithms in highly dynamic networks"
    - "distributed triangle detection via expander decomposition"
    - "improved algorithms for fully dynamic maximal independent set"
    - "detecting cliques in congest networks"
    - "distributed subgraph detection"

Connected through hyperedge HE-5 (induced by source paper "parameterized distributed complexity theory a logical approach"):
  with members:
    - "almost global problems in the local model"
    - "parameterized distributed algorithms"
    - "progressive algorithms for domination and independence"
    - "first order interpretations of bounded expansion classes"
    - "new classes of distributed time complexity"
    - "a time hierarchy theorem for the local model"
    - "towards a complexity theory for the congested clique"
    - "survey of distributed decision"

Connected through hyperedge HE-6 (induced by source paper "the communication complexity of set intersection and multiple equality testing"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "distributed triangle detection via expander decomposition"
    - "detecting cliques in congest networks"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "triangle finding and listing in congest networks"
    - "communication complexity for algorithm designers"
    - "on the communication complexity of sparse set disjointness and exists equal problems"

Connected through hyperedge HE-7 (induced by source paper "brief announcement does preprocessing help under congestion"):
  with members:
    - "smaller cuts higher lower bounds"

Connected through hyperedge HE-8 (induced by source paper "distributed triangle detection via expander decomposition"):
  with members:
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "detecting cliques in congest networks"
    - "mst in o 1 rounds of the congested clique"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "on the distributed complexity of large scale graph computations"

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)