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: "dense subgraph maintenance under streaming edge weight updates for real time story identification"
  Abstract: Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, every day, millions of blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly-coupled real-world entities, namely the people, locations, products, etc., that are involved in the story. The sheer scale, and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time. The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightly-coupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DYNDENS, which outperforms adaptations of existing techniques to this setting, and yields meaningful results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.

Connected through hyperedge HE-1 (induced by source paper "mining maximal span cores from temporal networks"):
  with members:
    - "best friends forever bff finding lasting dense subgraphs"
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "social resilience in online communities the autopsy of friendster"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "mining density contrast subgraphs"):
  with members:
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "alid scalable dominant cluster detection"
    - "deltacon a principled massive graph similarity function"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-3 (induced by source paper "the densest subgraph problem with a convex concave size function"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-4 (induced by source paper "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "single pass spectral sparsification in dynamic streams"
    - "densest subgraph in streaming and mapreduce"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "perfect matchings in o n 1 5 time in regular bipartite graphs"

Connected through hyperedge HE-5 (induced by source paper "finding the hierarchy of dense subgraphs using nucleus decompositions"):
  with members:
    - "a novel approach to finding near cliques the triangle densest subgraph problem"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "decompositions of triangle dense graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-6 (induced by source paper "alid scalable dominant cluster detection"):
  with members:
    - "scalable k means"

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)