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: "capacitated team formation problem on social networks"
  Abstract: In a team formation problem, one is required to find a group of users that can match the requirements of a collaborative task. Example of such collaborative tasks abound, ranging from software product development to various participatory sensing tasks in knowledge creation. Due to the nature of the task, team members are often required to work on a co-operative basis. Previous studies have indicated that co-operation becomes effective in presence of social connections. Therefore, effective team selection requires the team members to be socially close as well as a division of the task among team members so that no user is overloaded by the assignment. In this work, we investigate how such teams can be formed on a social network. #R##N#Since our team formation problems are proven to be NP-hard, we design efficient approximate algorithms for finding near optimum teams with provable guarantees. As traditional data-sets from on-line social networks (e.g. Twitter, Facebook etc) typically do not contain instances of large scale collaboration, we have crawled millions of software repositories spanning a period of four years and hundreds of thousands of developers from GitHub, a popular open-source social coding network. We perform large scale experiments on this data-set to evaluate the accuracy and efficiency of our algorithms. Experimental results suggest that our algorithms achieve significant improvement in finding effective teams, as compared to naive strategies and scale well with the size of the data. Finally, we provide a validation of our techniques by comparing with existing software teams in GitHub.

Connected through hyperedge HE-1 (induced by source paper "algorithms for fair team formation in online labour marketplaces"):
  with members:
    - "fair clustering through fairlets"
    - "multi skill collaborative teams based on densest subgraphs"

Connected through hyperedge HE-2 (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"
    - "multi skill collaborative teams based on densest subgraphs"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-3 (induced by source paper "specialty aware task assignment in spatial crowdsourcing"):
  with members:
    - "a real time framework for task assignment in hyperlocal spatial crowdsourcing"
    - "task assignment on multi skill oriented spatial crowdsourcing"

Connected through hyperedge HE-4 (induced by source paper "team formation for scheduling educational material in massive online classes"):
  with members:
    - "finding low tension communities"
    - "towards realistic team formation in social networks based on densest subgraphs"

Connected through hyperedge HE-5 (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"
    - "multi skill collaborative teams based on densest subgraphs"

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)