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 "evaluating balance on social networks from their simple cycles"):
  Abstract: Signed networks have long been used to represent social relations of amity (+) and enmity (-) between individuals. Group of individuals who are cyclically connected are said to be balanced if the number of negative edges in the cycle is even and unbalanced otherwise. In its earliest and most natural formulation, the balance of a social network was thus defined from its simple cycles, cycles which do not visit any vertex more than once. Because of the inherent difficulty associated with finding such cycles on very large networks, social balance has since then been studied via other means. In this article we present the balance as measured from the simple cycles and primitive orbits of social networks. We specifically provide two measures of balance: the proportion R` of negative simple cycles of length ` for each ` 6 20 which generalises the triangle index, and a ratio K` which extends the relative signed clustering coefficient introduced by Kunegis. To do so, we use a Monte Carlo implementation of a novel exact formula for counting the simple cycles on any weighted directed graph. Our method is free from the double-counting problem affecting previous cycle-based approaches, does not require edge-reciprocity of the underlying network, provides a gray-scale measure of balance for each cycle length separately and is sufficiently tractable that it can be implemented on a standard desktop computer. We observe that social networks exhibit strong inter-edge correlations favouring balanced situations and we determine the corresponding correlation length x . For longer simple cycles, R` undergoes a sharp transition to values expected from an uncorrelated model. This transition is absent from synthetic random networks, strongly suggesting that it carries a sociological meaning warranting further research.

Includes members:
  Member "measuring partial balance in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "a general purpose algorithm for counting simple cycles and simple paths of any length":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")
  Member "an exact method for computing the frustration index in signed networks using binary programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting coalitions by optimally partitioning signed networks of political collaboration")
  Member "a modelling and computational study of the frustration index in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating weighted max 2 sat instances of tunable difficulty with frustrated loops")
  Member "a polynomial delay algorithm for generating connected induced subgraphs of a given cardinality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a general purpose algorithm for counting simple cycles and simple paths of any length")
  Member "applications of structural balance in signed social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "signed network structural analysis and applications with a focus on balance theory")

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)