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 "semi blind inference of topologies and dynamical processes over dynamic graphs"):
  Abstract: A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations. Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations. Structural equation models (SEMs) and structural vector autoregressive models (SVARMs) have well documented merits in identifying even directed topologies of complex graphs; while SEMs capture contemporaneous causal dependencies among nodes, SVARMs further account for time-lagged influences. A batch solver is proposed that iterates between inferring directed graphs that “best” fit the sequence of observations, and estimating the network processes at reduced computational complexity by leveraging tools related to Kalman smoothing. To further accommodate delay-sensitive applications, an online joint inference approach is put forth that even tracks time-evolving topologies. Furthermore, we specify novel conditions for identifying the network topology given partial observations. We prove that the required number of observations for unique identification reduces significantly when the network structure is sparse. Numerical tests with synthetic as well as real datasets corroborate the effectiveness of the proposed approach.

Includes members:
  Member "stationary graph processes and spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "kernel based reconstruction of space time functions on dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "efficient sampling set selection for bandlimited graph signals using graph spectral proxies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edgenets edge varying graph neural networks")
  Member "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "localized iterative methods for interpolation in graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prediction and sampling with local graph transforms for quasi lossless light field compression")
  Member "clustering partially observed graphs via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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.LG(Machine Learning)