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 in hierarchical social networks"):
  Abstract: We study a social network consisting of agents organized as a hierarchical   $M$  -ary rooted tree, common in enterprise and military organizational structures. The goal is to aggregate information to solve a binary hypothesis testing problem. Each agent at a leaf of the tree, and only such an agent, makes a direct measurement of the underlying true hypothesis. The leaf agent then generates a message and sends it to its supervising agent, at the next level of the tree. Each supervising agent aggregates the messages from the    $M$   members of its group, produces a summary message, and sends it to its supervisor at the next level, and so on. Ultimately, the agent at the root of the tree makes an overall decision. We derive upper and lower bounds for the Type I and Type II error probabilities associated with this decision with respect to the number of leaf agents, which in turn characterize the converge rates of the Type I, Type II, and total error probabilities. We also provide a message-passing scheme involving non-binary message alphabets and characterize the exponent of the error probability with respect to the message alphabet size.

Includes members:
  Member "detection performance in balanced binary relay trees with node and link failures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "average error probability in wireless sensor networks with imperfect sensing and communication for different decision rules")
  Member "submodularity and optimality of fusion rules in balanced binary relay trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "string submodular functions with curvature constraints")
  Member "detection performance of m ary relay trees with non binary message alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detection performance in balanced binary relay trees with node and link failures")
  Member "rate of learning in hierarchical social networks":
    also connected through other hyperedges:
      (none)
  Member "subexponential convergence for information aggregation on regular trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asynchronous majority dynamics in preferential attachment trees")
  Member "error probability bounds for balanced binary relay trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "average error probability in wireless sensor networks with imperfect sensing and communication for different decision rules")
  Member "data fusion trees for detection does architecture matter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed detection with empirically observed statistics")

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)