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: "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.

Connected through hyperedge HE-1 (induced by source paper "asynchronous majority dynamics in preferential attachment trees"):
  with members:
    - "stabilizing consensus with many opinions"
    - "reaching consensus via non bayesian asynchronous learning in social networks"
    - "subexponential convergence for information aggregation on regular trees"

Connected through hyperedge HE-2 (induced by source paper "on the relationship between inference and data privacy in decentralized iot networks"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "differentially private iterative synchronous consensus"
    - "privacy against statistical inference"
    - "the value of feedback in decentralized detection"

Connected through hyperedge HE-3 (induced by source paper "robust decentralized detection and social learning in tandem networks"):
  with members:
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "on learning with finite memory"
    - "error probability bounds for balanced binary relay trees"

Connected through hyperedge HE-4 (induced by source paper "hypothesis testing in feedforward networks with broadcast failures"):
  with members:
    - "on learning with finite memory"
    - "error probability bounds for balanced binary relay trees"

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)