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: "near linear lower bounds for distributed distance computations even in sparse networks"
  Abstract: We develop a new technique for constructing sparse graphs that allow us to prove near-linear lower bounds on the round complexity of computing distances in the CONGEST model. Specifically, we show an $\widetilde{\Omega}(n)$ lower bound for computing the diameter in sparse networks, which was previously known only for dense networks [Frishknecht et al., SODA 2012]. In fact, we can even modify our construction to obtain graphs with constant degree, using a simple but powerful degree-reduction technique which we define. #R##N#Moreover, our technique allows us to show $\widetilde{\Omega}(n)$ lower bounds for computing $(\frac{3}{2}-\varepsilon)$-approximations of the diameter or the radius, and for computing a $(\frac{5}{3}-\varepsilon)$-approximation of all eccentricities. For radius, we are unaware of any previous lower bounds. For diameter, these greatly improve upon previous lower bounds and are tight up to polylogarithmic factors [Frishknecht et al., SODA 2012], and for eccentricities the improvement is both in the lower bound and in the approximation factor [Holzer and Wattenhofer, PODC 2012]. #R##N#Interestingly, our technique also allows showing an almost-linear lower bound for the verification of $(\alpha,\beta)$-spanners, for $\alpha < \beta+1$.

Connected through hyperedge HE-1 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest"):
  with members:
    - "improved distributed approximation to maximum independent set"
    - "detecting cliques in congest networks"
    - "distributed spanner approximation"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "distributed approximation of maximum independent set and maximum matching"
    - "distributed construction of purely additive spanners"
    - "approximation of distances and shortest paths in the broadcast congest clique"

Connected through hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "expander decomposition and pruning faster stronger and simpler"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"

Connected through hyperedge HE-3 (induced by source paper "fast approximate shortest paths in the congested clique"):
  with members:
    - "delta 1 coloring in the congested clique model"
    - "congested clique algorithms for graph spanners"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed triangle detection via expander decomposition"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "mst in o 1 rounds of the congested clique"

Connected through hyperedge HE-4 (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-5 (induced by source paper "smaller cuts higher lower bounds"):
  with members:
    - "a faster deterministic distributed algorithm for weighted apsp through pipelining"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "optimal distributed coloring algorithms for planar graphs in the local model"
    - "distributed exact shortest paths in sublinear time"
    - "a time hierarchy theorem for the local model"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "quadratic and near quadratic lower bounds for the congest model"

Connected through hyperedge HE-6 (induced by source paper "hardness of distributed optimization"):
  with members:
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "fast and efficient distributed computation of hamiltonian cycles in random graphs"
    - "a distributed algorithm for finding hamiltonian cycles in random graphs in o log n time"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "parameterized distributed algorithms"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "detecting cliques in congest networks"
    - "simple and local independent set approximation"

Connected through hyperedge HE-7 (induced by source paper "shortest paths in a hybrid network model"):
  with members:
    - "fast approximate shortest paths in the congested clique"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "mst in o 1 rounds of the congested clique"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "improved distributed algorithms for exact shortest paths"
    - "competitive routing in hybrid communication networks"

Connected through hyperedge HE-8 (induced by source paper "planar diameter via metric compression"):
  with members:
    - "exact distance oracles for planar graphs with failing vertices"
    - "minor excluded network families admit fast distributed algorithms"
    - "distributed treewidth computation"
    - "faster distributed shortest path approximations via shortcuts"
    - "constant query time 1 epsilon approximate distance oracle for planar graphs"
    - "fast and compact exact distance oracle for planar graphs"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "better tradeoffs for exact distance oracles in planar graphs"

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)