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 novel approach to finding near cliques the triangle densest subgraph problem"
  Abstract: Many graph mining applications rely on detecting subgraphs which are near-cliques. There exists a dichotomy between the results in the existing work related to this problem: on the one hand the densest subgraph problem (DSP) which maximizes the average degree over all subgraphs is solvable in polynomial time but for many networks fails to find subgraphs which are near-cliques. On the other hand, formulations that are geared towards finding near-cliques are NP-hard and frequently inapproximable due to connections with the Maximum Clique problem. #R##N#In this work, we propose a formulation which combines the best of both worlds: it is solvable in polynomial time and finds near-cliques when the DSP fails. Surprisingly, our formulation is a simple variation of the DSP. Specifically, we define the triangle densest subgraph problem (TDSP): given $G(V,E)$, find a subset of vertices $S^*$ such that $\tau(S^*)=\max_{S \subseteq V} \frac{t(S)}{|S|}$, where $t(S)$ is the number of triangles induced by the set $S$. We provide various exact and approximation algorithms which the solve the TDSP efficiently. Furthermore, we show how our algorithms adapt to the more general problem of maximizing the $k$-clique average density. Finally, we provide empirical evidence that the TDSP should be used whenever the output of the DSP fails to output a near-clique.

Connected through hyperedge HE-1 (induced by source paper "near optimal fully dynamic densest subgraph"):
  with members:
    - "distributed dense subgraph detection and low outdegree orientation"
    - "new algorithms and lower bounds for all pairs max flow in undirected graphs"
    - "inapproximability of maximum biclique problems minimum k cut and densest at least k subgraph from the small set expansion hypothesis"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "egalitarian graph orientations"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "popular conjectures as a barrier for dynamic planar graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "finding events in temporal networks segmentation meets densest subgraph discovery"):
  with members:
    - "strongly polynomial efficient approximation scheme for segmentation"
    - "evidense a graph based method for finding unique high impact events with succinct keyword based descriptions"
    - "local community detection in dynamic networks"
    - "best friends forever bff finding lasting dense subgraphs"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-3 (induced by source paper "enumerating top k quasi cliques"):
  with members:
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-4 (induced by source paper "clique counting in mapreduce algorithms and experiments"):
  with members:
    - "subgraph enumeration in massive graphs"
    - "the input output complexity of triangle enumeration"
    - "enumerating subgraph instances using map reduce"
    - "space round tradeoffs for mapreduce computations"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "sorting searching and simulation in the mapreduce framework"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-5 (induced by source paper "density friendly graph decomposition"):
  with members:
    - "discovering nested communities"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition a tool for the visualization of large scale networks"

Connected through hyperedge HE-6 (induced by source paper "finding the hierarchy of dense subgraphs using nucleus decompositions"):
  with members:
    - "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"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"

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)