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: "local algorithms for hierarchical dense subgraph discovery"
  Abstract: Finding the dense regions of a graph and relations among them is a fundamental problem in network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical relations. The incremental nature of algorithms for computing these decompositions and the need for global information at each step of the algorithm hinders scalable parallelization and approximations since the densest regions are not revealed until the end. In a previous work, Lu et al. proposed to iteratively compute the $h$-indices of neighbor vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. This work generalizes the iterative $h$-index computation for truss decomposition as well as nucleus decomposition which leverages higher-order structures to generalize core and truss decompositions. In addition, we prove convergence bounds on the number of iterations. We present a framework of local algorithms to obtain the core, truss, and nucleus decompositions. Our algorithms are local, parallel, offer high scalability, and enable approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our local algorithms on real-world networks.

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"
    - "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 large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for densest subgraph discovery"):
  with members:
    - "exploring communities in large profiled graphs"
    - "a survey of community search over big graphs"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "density friendly graph decomposition"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "mathematical and algorithmic analysis of network and biological data"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-3 (induced by source paper "theoretically efficient parallel graph algorithms can be fast and scalable"):
  with members:
    - "single source bottleneck path algorithm faster than sorting for sparse graphs"
    - "parallelism in randomized incremental algorithms"
    - "bigsparse high performance external graph analytics"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "nearly work efficient parallel algorithm for digraph reachability"
    - "a new efficient construction on probabilistic tree embeddings"
    - "compressing graphs and indexes with recursive graph bisection"
    - "the gap benchmark suite"

Connected through hyperedge HE-4 (induced by source paper "shared memory parallel maximal clique enumeration"):
  with members:
    - "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 large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal 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.SI(Social and Information Networks)