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 "when less is more systematic analysis of cascade based community detection"):
  Abstract: Information diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe when nodes become infected, but the underlying network, over which a contagion or information propagates, is hidden. Inferring properties of the underlying network is important since these properties can be used for constraining infections, forecasting, viral marketing, etc. Moreover, for many applications, it is sufficient to recover only coarse high-level properties of this network rather than all its edges. In this paper, we conduct a systematic and extensive analysis of the following problem: given only the infection times, find communities of highly interconnected nodes. We carry out a thorough comparison between existing and new approaches on several large datasets and cover methodological challenges that are specific to this problem. One of the main conclusions is that the most stable performance and the most significant improvement on the current state-of-the-art are achieved by our proposed simple heuristic approaches that are agnostic to a particular graph structure and epidemic model. We also show that some well-known community detection algorithms can be enhanced by including edge weights based on the cascade data.

Includes members:
  Member "systematic analysis of cluster similarity indices towards bias free cluster validation":
    also connected through other hyperedges:
      (none)
  Member "community detection through likelihood optimization in search of a sound model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "publishing community preserving attributed social graphs with a differential privacy guarantee")
  Member "dani a fast diffusion aware network inference algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian inference of network structure from information cascades")
  Member "equivalence between modularity optimization and maximum likelihood methods for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mga momentum gradient attack on network")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "community detection in node attributed social networks a survey")
  Member "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "modeling information propagation with survival theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "structure and dynamics of information pathways in online media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning mixtures of graphs from epidemic cascades")

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)