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 "a distributed tracking algorithm for reconstruction of graph signals"):
  Abstract: The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. Designing efficient distributed algorithms is critical for this task. This paper focuses on the distributed reconstruction of a time-varying bandlimited graph signal based on observations sampled at a subset of selected nodes. A distributed least square reconstruction (DLSR) algorithm is proposed to recover the unknown signal iteratively, by allowing neighboring nodes to communicate with one another and make fast updates. DLSR uses a decay scheme to annihilate the out-of-band energy occurring in the reconstruction process, which is inevitably caused by the transmission delay in distributed systems. Proof of convergence and error bounds for DLSR are provided in this paper, suggesting that the algorithm is able to track time-varying graph signals and perfectly reconstruct time-invariant signals. The DLSR algorithm is numerically experimented with synthetic data and real-world sensor network data, which verifies its ability in tracking slowly time-varying graph signals.

Includes members:
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "a spectral graph uncertainty principle":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "a multiscale pyramid transform for graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "copt coordinated optimal transport on graphs")
  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-4 (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-5 (induced by source paper "semi blind inference of topologies and dynamical processes over dynamic graphs")
  Member "discrete signal processing on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "edgenets edge varying graph neural networks")
  Member "perfect reconstruction two channel wavelet filter banks for graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering")
  Member "variational splines and paley wiener spaces on combinatorial graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graphbgs background subtraction via recovery of graph signals")

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)