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 local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
  Abstract: We study the design of local algorithms for massive graphs. A local algorithm is one that finds a solution containing or near a given vertex without looking at the whole graph. We present a local clustering algorithm. Our algorithm finds a good cluster--a subset of vertices whose internal connections are significantly richer than its external connections--near a given vertex. The running time of our algorithm, when it finds a non-empty local cluster, is nearly linear in the size of the cluster it outputs. #R##N#Our clustering algorithm could be a useful primitive for handling massive graphs, such as social networks and web-graphs. As an application of this clustering algorithm, we present a partitioning algorithm that finds an approximate sparsest cut with nearly optimal balance. Our algorithm takes time nearly linear in the number edges of the graph. #R##N#Using the partitioning algorithm of this paper, we have designed a nearly-linear time algorithm for constructing spectral sparsifiers of graphs, which we in turn use in a nearly-linear time algorithm for solving linear systems in symmetric, diagonally-dominant matrices. The linear system solver also leads to a nearly linear-time algorithm for approximating the second-smallest eigenvalue and corresponding eigenvector of the Laplacian matrix of a graph. These other results are presented in two companion papers.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "optimizing high performance markov clustering for pre exascale architectures"):
  with members:
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-3 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "improved distributed expander decomposition and nearly optimal triangle enumeration"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "mst in o 1 rounds of the congested clique"
    - "fooling views a new lower bound technique for distributed computations under congestion"

Connected through hyperedge HE-5 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  with members:
    - "testing graph clusterability algorithms and lower bounds"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "learning communities in the presence of errors"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"

Connected through hyperedge HE-6 (induced by source paper "learning resolution parameters for graph clustering"):
  with members:
    - "a parallel projection method for metric constrained optimization"
    - "correlation clustering generalized"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"
    - "an optimization approach to locally biased graph algorithms"
    - "higher order organization of complex networks"

Connected through hyperedge HE-7 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"

Connected through hyperedge HE-8 (induced by source paper "breaking quadratic time for small vertex connectivity and an approximation scheme"):
  with members:
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "expander decomposition and pruning faster stronger and simpler"
    - "capacity releasing diffusion for speed and locality"
    - "local flow partitioning for faster edge connectivity"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "a simple and strongly local flow based method for cut improvement"
    - "fully dynamic minimum spanning forest with improved worst case update time"
    - "deterministic global minimum cut of a simple graph in near linear time"

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.DS(Data Structures and Algorithms)