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: "multi skill collaborative teams based on densest subgraphs"
  Abstract: We consider the problem of identifying a team of skilled individuals for collaboration, in the presence of a social network. Each node in the social network may be an expert in one or more skills. Edge weights specify affinity or collaborative compatibility between respective nodes. Given a project that requires a set of specified number of skilled individuals in each area of expertise, the goal is to identify a team that maximizes the collaborative compatibility. For example, the requirement may be to form a team that has at least three databases experts and at least two theory experts. We explore team formation where the collaborative compatibility objective is measured as the density of the induced subgraph on selected nodes. The problem of maximizing density is NP-hard even when the team requires individuals of only one skill. We present a 3-approximation algorithm that improves upon a naive extension of the previously known algorithm for densest at least $k$ subgraph problem. We further show how the same approximation can be extended to a special case of multiple skills. Our problem generalizes the formulation studied by Lappas et al. [KDD '09] who measure team compatibility in terms of diameter or spanning tree costs. Experiments are performed on a crawl of the DBLP graph where individuals can be skilled in at most four areas - theory, databases, data mining, and artificial intelligence. In addition to our main algorithm, we also present heuristic extensions to trade off between the size of the solution and its induced density. These density-based algorithms outperform the diameter-based objective on several metrics for assessing the collaborative compatibility of teams. The solutions suggested are also intuitively meaningful and scale well with the increase in the number of skilled individuals required.

Connected through hyperedge HE-1 (induced by source paper "a team formation algorithm for faultline minimization"):
  with members:
    - "finding low tension communities"
    - "team formation for scheduling educational material in massive online classes"
    - "towards realistic team formation in social networks based on densest subgraphs"

Connected through hyperedge HE-2 (induced by source paper "the guided team partitioning problem definition complexity and algorithm"):
  with members:
    - "a team formation algorithm for faultline minimization"
    - "team formation for scheduling educational material in massive online classes"
    - "towards realistic team formation in social networks based on densest subgraphs"

Connected through hyperedge HE-3 (induced by source paper "algorithms for fair team formation in online labour marketplaces"):
  with members:
    - "fair clustering through fairlets"
    - "capacitated team formation problem on social networks"

Connected through hyperedge HE-4 (induced by source paper "risk averse matchings over uncertain graph databases"):
  with members:
    - "predicting positive and negative links with noisy queries theory practice"
    - "uncertain graph sparsification"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "subgraph pattern matching over uncertain graphs with identity linkage uncertainty"
    - "injecting uncertainty in graphs for identity obfuscation"
    - "capacitated team formation problem on social networks"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-5 (induced by source paper "finding low tension communities"):
  with members:
    - "the minimum wiener connector problem"
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "opinion maximization in social networks"
    - "how bad is forming your own opinion"

Connected through hyperedge HE-6 (induced by source paper "attribute truss community search"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "approximate closest community search in networks"
    - "truss decomposition in massive networks"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-7 (induced by source paper "profit aware team grouping in social networks a generalized cover decomposition approach"):
  with members:
    - "optimally approximating the coverage lifetime of wireless sensor networks"

Connected through hyperedge HE-8 (induced by source paper "the whole is greater than the sum of its parts optimization in collaborative crowdsourcing"):
  with members:
    - "optimization in knowledge intensive crowdsourcing"
    - "capacitated team formation problem on social networks"

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.SI(Social and Information Networks)