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 "learning mixtures of graphs from epidemic cascades"):
  Abstract: We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algorithmic development with rigorous guarantees has lagged. Graph mixtures are apparently no exception: until now, very little is known about whether this problem is solvable.  To the best of our knowledge, we establish the first necessary and sufficient conditions for this problem to be solvable in polynomial time on edge-separated graphs. When the conditions are met, i.e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors).  We give complimentary results and provide sample-optimal (up to log factors) algorithms for mixtures of directed graphs of out-degree at least three, for mixture of undirected graphs of unbalanced and/or unknown priors.

Includes members:
  Member "learning graphs from noisy epidemic cascades":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling mixtures of epidemics on graphs")
  Member "meta learning with relational information for short sequences":
    also connected through other hyperedges:
      (none)
  Member "em converges for a mixture of many linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "the cost of uncertainty in curing epidemics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning graphs from noisy epidemic cascades")
  Member "fake news mitigation via point process based intervention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cluster based social reinforcement learning")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning for mixed linear regression")
  Member "characterization and inference of graph diffusion processes from observations of stationary signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "solving a mixture of many random linear equations by tensor decomposition and alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning for mixed linear regression")

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)