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: "listing all maximal cliques in large sparse real world graphs"
  Abstract: We implement a new algorithm for listing all maximal cliques in sparse graphs due to Eppstein, L\"offler, and Strash (ISAAC 2010) and analyze its performance on a large corpus of real-world graphs. Our analysis shows that this algorithm is the first to offer a practical solution to listing all maximal cliques in large sparse graphs. All other theoretically-fast algorithms for sparse graphs have been shown to be significantly slower than the algorithm of Tomita et al. (Theoretical Computer Science, 2006) in practice. However, the algorithm of Tomita et al. uses an adjacency matrix, which requires too much space for large sparse graphs. Our new algorithm opens the door for fast analysis of large sparse graphs whose adjacency matrix will not fit into working memory.

Connected through hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs"):
  with members:
    - "enumerating top k quasi cliques"
    - "efficiently enumerating all maximal cliques with bit parallelism"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "shared memory graph truss decomposition"
    - "incremental maintenance of maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "faster sublinear approximations of k cliques for low arboricity graphs"
    - "lower bounds for approximating graph parameters via communication complexity"
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-3 (induced by source paper "resolving marker pose ambiguity by robust rotation averaging with clique constraints"):
  with members:
    - "mapping and localization from planar markers"
    - "an open source fiducial based visual inertial motion capture system"

Connected through hyperedge HE-4 (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"
    - "graphlab a new framework for parallel machine learning"
    - "a local algorithm for finding dense subgraphs"

Connected through hyperedge HE-5 (induced by source paper "shared memory parallel maximal clique enumeration"):
  with members:
    - "local algorithms for hierarchical dense subgraph discovery"
    - "shared memory graph truss decomposition"
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-6 (induced by source paper "on the enumeration of maximal delta gamma cliques of a temporal network"):
  with members:
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "mining maximal cliques from an uncertain graph"

Connected through hyperedge HE-7 (induced by source paper "efficient enumeration of bipartite subgraphs in graphs"):
  with members:
    - "enumeration of enumeration algorithms"
    - "efficient enumeration of induced subtrees in a k degenerate graph"
    - "optimal listing of cycles and st paths in undirected graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-8 (induced by source paper "found graph data and planted vertex covers"):
  with members:
    - "sum of squares lower bounds for planted clique"
    - "exact recovery in the stochastic block model"
    - "deltacon a principled massive graph similarity function"

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)