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: "compressing graphs and indexes with recursive graph bisection"
  Abstract: Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes.   We extend the recent theoretical model of Chierichetti et al. (KDD 2009) for graph compression, and show how it can be employed for compression-friendly reordering of social networks and web graphs and for assigning document identifiers in inverted indexes. We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection.   Our experiments show a significant improvement of the compression rate of graph and indexes over existing heuristics. The new method is relatively simple and allows efficient parallel and distributed implementations, which is demonstrated on graphs with billions of vertices and hundreds of billions of edges.

Connected through hyperedge HE-1 (induced by source paper "techniques for inverted index compression"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "on slicing sorted integer sequences"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"

Connected through hyperedge HE-2 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent"):
  with members:
    - "vebo a vertex and edge balanced ordering heuristic to load balance parallel graph processing"
    - "matrix completion has no spurious local minimum"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "distributed balanced partitioning via linear embedding"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "spinner scalable graph partitioning in the cloud"
    - "partitioning complex networks via size constrained clustering"
    - "nonuniform graph partitioning with unrelated weights"

Connected through hyperedge HE-3 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations"):
  with members:
    - "grammar based graph compression"
    - "compressing networks with super nodes"
    - "graphzip dictionary based compression for mining graph streams"
    - "parallel construction of compact planar embeddings"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "fully dynamic de bruijn graphs"
    - "on the implicit graph conjecture"

Connected through hyperedge HE-4 (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"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "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"
    - "the gap benchmark suite"

Connected through hyperedge HE-5 (induced by source paper "efficient butterfly counting for large bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "butterfly counting in bipartite networks"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triadic measures on graphs the power of wedge sampling"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-6 (induced by source paper "vertex priority based butterfly counting for large scale bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "peeling bipartite networks for dense subgraph discovery"
    - "measuring and modeling bipartite graphs with community structure"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triadic measures on graphs the power of wedge sampling"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "boosting search performance using query variations"):
  with members:
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "query driven algorithm selection in early stage retrieval"

Connected through hyperedge HE-8 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "network structure inference a survey motivations methods and applications"
    - "efficient frequent directions algorithm for sparse matrices"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"
    - "line large scale information network embedding"

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)