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: "gsketch on query estimation in graph streams"
  Abstract: Many dynamic applications are built upon large network infrastructures, such as social networks, communication networks, biological networks and the Web. Such applications create data that can be naturally modeled as graph streams, in which edges of the underlying graph are received and updated sequentially in a form of a stream. It is often necessary and important to summarize the behavior of graph streams in order to enable effective query processing. However, the sheer size and dynamic nature of graph streams present an enormous challenge to existing graph management techniques. In this paper, we propose a new graph sketch method, gSketch, which combines well studied synopses for traditional data streams with a sketch partitioning technique, to estimate and optimize the responses to basic queries on graph streams. We consider two different scenarios for query estimation: (1) A graph stream sample is available; (2) Both a graph stream sample and a query workload sample are available. Algorithms for different scenarios are designed respectively by partitioning a global sketch to a group of localized sketches in order to optimize the query estimation accuracy. We perform extensive experimental studies on both real and synthetic data sets and demonstrate the power and robustness of gSketch in comparison with the state-of-the-art global sketch method.

Connected through hyperedge HE-1 (induced by source paper "fast and accurate graph stream summarization"):
  with members:
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "how hard is counting triangles in the streaming model"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-2 (induced by source paper "composite hashing for data stream sketches"):
  with members:
    - "on sampling from massive graph streams"
    - "learning to hash for indexing big data a survey"
    - "stream sampling for frequency cap statistics"

Connected through hyperedge HE-3 (induced by source paper "graphzip dictionary based compression for mining graph streams"):
  with members:
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-4 (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"
    - "compressing graphs and indexes with recursive graph bisection"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on summarizing graph streams"):
  with members:
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "how hard is counting triangles in the streaming model"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-6 (induced by source paper "a selectivity based approach to continuous pattern detection in streaming graphs"):
  with members:
    - "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-7 (induced by source paper "graph sample and hold a framework for big graph analytics"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "network sampling from static to streaming graphs"
    - "social structure of facebook networks"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-8 (induced by source paper "query optimization for dynamic graphs"):
  with members:
    - "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
    - "streamworks a system for dynamic graph search"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"

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.DB(Databases)