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 selectivity based approach to continuous pattern detection in streaming graphs"
  Abstract: Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.

Connected through hyperedge HE-1 (induced by source paper "updates aware graph pattern based node matching"):
  with members:
    - "time constrained continuous subgraph search over streaming graphs"
    - "incremental maintenance of maximal cliques in a dynamic graph"

Connected through hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-3 (induced by source paper "a survey and experimental analysis of distributed subgraph matching"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "communication steps for parallel query processing"
    - "enumerating subgraph instances using map reduce"
    - "efficient subgraph matching on billion node graphs"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-4 (induced by source paper "graphzero breaking symmetry for efficient graph mining"):
  with members:
    - "kaleido an efficient out of core graph mining system on a single machine"
    - "graph based anomaly detection and description a survey"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-5 (induced by source paper "efficient continuous multi query processing over graph streams"):
  with members:
    - "structural patterns of information cascades and their implications for dynamics and semantics"
    - "continuous queries for multi relational graphs"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-6 (induced by source paper "fast and accurate graph stream summarization"):
  with members:
    - "how hard is counting triangles in the streaming model"
    - "gsketch on query estimation in graph streams"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-7 (induced by source paper "loom query aware partitioning of online graphs"):
  with members:
    - "taper query aware partition enhancement for large heterogenous graphs"
    - "query workload based rdf graph fragmentation and allocation"
    - "provgen generating synthetic prov graphs with predictable structure"
    - "practical graph isomorphism ii"
    - "pt scotch a tool for efficient parallel graph ordering"

Connected through hyperedge HE-8 (induced by source paper "nous construction and querying of dynamic knowledge graphs"):
  with members:
    - "bayesian non exhaustive classification a case study online name disambiguation using temporal record streams"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "feature selection for classification under anonymity constraint"
    - "arabesque a system for distributed graph mining extended version"

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)