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 algorithm for finding dense subgraphs"
  Abstract: We present a local algorithm for finding dense subgraphs of bipartite graphs, according to the definition of density proposed by Kannan and Vinay. Our algorithm takes as input a bipartite graph with a specified starting vertex, and attempts to find a dense subgraph near that vertex. We prove that for any subgraph S with k vertices and density theta, there are a significant number of starting vertices within S for which our algorithm produces a subgraph S' with density theta / O(log n) on at most O(D k^2) vertices, where D is the maximum degree. The running time of the algorithm is O(D k^2), independent of the number of vertices in the graph.

Connected through hyperedge HE-1 (induced by source paper "an efficient system for subgraph discovery"):
  with members:
    - "local community detection in dynamic networks"
    - "discriminative subnetworks with regularized spectral learning for global state network data"
    - "mining frequent neighborhood patterns in a large labeled graph"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "defining and evaluating network communities based on ground truth"
    - "listing all maximal cliques in large sparse real world graphs"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-2 (induced by source paper "a divide and conquer algorithm for betweenness centrality"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "detecting and characterizing small dense bipartite like subgraphs by the bipartiteness ratio measure"):
  with members:
    - "many sparse cuts via higher eigenvalues"
    - "finding endogenously formed communities"
    - "markov chain methods for small set expansion"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "finding overlapping communities in social networks toward a rigorous approach"
    - "empirical comparison of algorithms for network community detection"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"

Connected through hyperedge HE-4 (induced by source paper "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"):
  with members:
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "finding sparse cuts locally using evolving sets"
    - "spectral sparsification of graphs"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "on the np completeness of some graph cluster measures"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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)