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 graphs from linear measurements fundamental trade offs and applications"):
  Abstract: We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We study fundamental trade-offs between the number of measurements (sample complexity), the complexity of the graph class, and the probability of error by first deriving a necessary condition (fundamental limit) on the number of measurements. Then, by considering a two-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity. In the special cases of the uniform distribution on trees with n nodes and the Erdos-Renyi (n,p) class, the fundamental trade-offs are tight up to multiplicative factors. Applying the Kirchhoff's matrix tree theorem, our results are extended to the scenario when part of the topology information is known a priori. In addition, we design and implement a polynomial-time (in n) algorithm based on the two-stage recovery scheme. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness of the proposed algorithm for accurate topology and parameter recovery.

Includes members:
  Member "learning graphs from data a signal representation perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "failure localization in power systems via tree partitions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "less is more real time failure localization in power systems")
  Member "learning identifiable gaussian bayesian networks in polynomial time and sample complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causality based feature selection methods and evaluations")
  Member "exact topology and parameter estimation in distribution grids with minimal observability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphical models in loopy distribution grids topology estimation change detection and limitation")
  Member "patopa a data driven parameter and topology joint estimation framework in distribution grids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physics guided deep neural networks for powerflow analysis")
  Member "learning linear structural equation models in polynomial time and sample complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stability of linear structural equation models of causal inference")
  Member "information theoretic limits of bayesian network structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integer programming for learning directed acyclic graphs from continuous data")
  Member "learning sparse graphs under smoothness prior":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")

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.IT(Information Theory)