human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  Abstract: In the densest subgraph problem, given a weighted undirected graph $G(V,E,w)$, with non-negative edge weights, we are asked to find a subset of nodes $S\subseteq V$ that maximizes the degree density $w(S)/|S|$, where $w(S)$ is the sum of the edge weights induced by $S$. This problem is a well studied problem, known as the {\em densest subgraph problem}, and is solvable in polynomial time. But what happens when the edge weights are negative? Is the problem still solvable in polynomial time? Also, why should we care about the densest subgraph problem in the presence of negative weights? #R##N#In this work we answer the aforementioned question. Specifically, we provide two novel graph mining primitives that are applicable to a wide variety of applications. Our primitives can be used to answer questions such as "how can we find a dense subgraph in Twitter with lots of replies and mentions but no follows?", "how do we extract a dense subgraph with high expected reward and low risk from an uncertain graph"? We formulate both problems mathematically as special instances of dense subgraph discovery in graphs with negative weights. We study the hardness of the problem, and we prove that the problem in general is NP-hard. We design an efficient approximation algorithm that works well in the presence of small negative weights, and also an effective heuristic for the more general case. Finally, we perform experiments on various real-world uncertain graphs, and a crawled Twitter multilayer graph that verify the value of the proposed primitives, and the practical value of our proposed algorithms. #R##N#The code and the data are available at \url{this https URL}.

Includes members:
  Member "robust densest subgraph discovery":
    also connected through other hyperedges:
      (none)
  Member "on finding dense common subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "twawler a lightweight twitter crawler":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "twittermancer predicting interactions on twitter accurately")
  Member "risk averse matchings over uncertain graph databases":
    also connected through other hyperedges:
      (none)
  Member "core decomposition in multilayer networks theory algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cores and other dense structures in complex networks")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeding with costly network information")
  Member "best friends forever bff finding lasting dense subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cores and other dense structures in complex networks")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)