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: "towards realistic team formation in social networks based on densest subgraphs"
  Abstract: Given a task $\mathcal{T}$, a set of experts $V$ with multiple skills and a social network $G(V, W)$ reflecting the compatibility among the experts, team formation is the problem of identifying a team $C \subseteq V$ that is both competent in performing the task $\mathcal{T}$ and compatible in working together. Existing methods for this problem make too restrictive assumptions and thus cannot model practical scenarios. The goal of this paper is to consider the team formation problem in a realistic setting and present a novel formulation based on densest subgraphs. Our formulation allows modeling of many natural requirements such as (i) inclusion of a designated team leader and/or a group of given experts, (ii) restriction of the size or more generally cost of the team (iii) enforcing locality of the team, e.g., in a geographical sense or social sense, etc. #R##N#The proposed formulation leads to a generalized version of the classical densest subgraph problem with cardinality constraints (DSP), which is an NP hard problem and has many applications in social network analysis. In this paper, we present a new method for (approximately) solving the generalized DSP (GDSP). Our method, FORTE, is based on solving an equivalent continuous relaxation of GDSP. The solution found by our method has a quality guarantee and always satisfies the constraints of GDSP. Experiments show that the proposed formulation (GDSP) is useful in modeling a broader range of team formation problems and that our method produces more coherent and compact teams of high quality. We also show, with the help of an LP relaxation of GDSP, that our method gives close to optimal solutions to GDSP.

Connected through hyperedge HE-1 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "from group recommendations to group formation"

Connected through hyperedge HE-2 (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"
    - "multi skill collaborative teams based on densest subgraphs"

Connected through hyperedge HE-3 (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"
    - "multi skill collaborative teams based on densest subgraphs"

Connected through hyperedge HE-4 (induced by source paper "synergistic team composition a computational approach to foster diversity in teams"):
  with members:
    - "an artificial intelligence tool for heterogeneous team formation in the classroom"

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

Connected through hyperedge HE-6 (induced by source paper "synergistic team composition"):
  with members:
    - "the composition and formation of effective teams computer science meets psychology"
    - "an artificial intelligence tool for heterogeneous team formation in the classroom"

Connected through hyperedge HE-7 (induced by source paper "team formation for scheduling educational material in massive online classes"):
  with members:
    - "finding low tension communities"
    - "capacitated team formation problem on social networks"

Connected through hyperedge HE-8 (induced by source paper "when engagement meets similarity efficient k r core computation on social networks"):
  with members:
    - "i o efficient core graph decomposition at web scale"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "locally estimating core numbers"
    - "geo social group queries with minimum acquaintance constraint"
    - "preventing unraveling in social networks gets harder"
    - "parameterized complexity of the anchored k core problem for directed graphs"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"

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)