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: "on finding dense common subgraphs"
  Abstract: We study the recently introduced problem of finding dense common subgraphs: Given a sequence of graphs that share the same vertex set, the goal is to find a subset of vertices $S$ that maximizes some aggregate measure of the density of the subgraphs induced by $S$ in each of the given graphs. Different choices for the aggregation function give rise to variants of the problem that were studied recently. We settle many of the questions left open by previous works, showing NP-hardness, hardness of approximation, non-trivial approximation algorithms, and an integrality gap for a natural relaxation.

Connected through hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "the complexity of finding small separators in temporal graphs"

Connected through hyperedge HE-2 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  with members:
    - "robust densest subgraph discovery"
    - "twawler a lightweight twitter crawler"
    - "risk averse matchings over uncertain graph databases"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "robust influence maximization"
    - "robust influence maximization"
    - "best friends forever bff finding lasting dense subgraphs"
    - "streaming graph partitioning in the planted partition model"

Connected through hyperedge HE-3 (induced by source paper "cores and other dense structures in complex networks"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a survey of signed network mining in social media"
    - "computing maximal cliques in link streams"

Connected through hyperedge HE-4 (induced by source paper "optimal average case reductions to sparse pca from weak assumptions to strong hardness"):
  with members:
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "the power of sum of squares for detecting hidden structures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "incoherence optimal matrix completion"
    - "label optimal regret bounds for online local learning"
    - "random tensors and planted cliques"

Connected through hyperedge HE-5 (induced by source paper "reducibility and computational lower bounds for problems with planted sparse structure"):
  with members:
    - "sherali adams integrality gaps matching the log density threshold"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"
    - "sum of squares lower bounds for refuting any csp"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "minimizing the union tight approximations for small set bipartite vertex expansion"

Connected through hyperedge HE-6 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "diversified coherent core search on multi layer graphs"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a fast order based approach for core maintenance"
    - "i o efficient core graph decomposition at web scale"
    - "the minimum wiener connector problem"

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)