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: "sampling high dimensional bandlimited fields on low dimensional manifolds"
  Abstract: Consider the task of sampling and reconstructing a bandlimited spatial field in $\Re^2$ using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to increase the total distance traveled by the sensors per unit area, which we call the \emph{path density}. In this paper we introduce the problem of designing sensor trajectories that are minimal in path density subject to the condition that the measurements of the field on these trajectories admit perfect reconstruction of bandlimited fields. We study various possible designs of sampling trajectories. Generalizing some ideas from the classical theory of sampling on lattices, we obtain necessary and sufficient conditions on the trajectories for perfect reconstruction. We show that a single set of equispaced parallel lines has the lowest path density from certain restricted classes of trajectories that admit perfect reconstruction. #R##N#We then generalize some of our results to higher dimensions. We first obtain results on designing sampling trajectories in higher dimensional fields. Further, interpreting trajectories as 1-dimensional manifolds, we extend some of our ideas to higher dimensional sampling manifolds. We formulate the problem of designing $\kappa$-dimensional sampling manifolds for $d$-dimensional spatial fields that are minimal in \emph{manifold density}, a natural generalization of the path density. We show that our results on sampling trajectories for fields in $\Re^2$ can be generalized to analogous results on $d-1$-dimensional sampling manifolds for $d$-dimensional spatial fields.

Connected through hyperedge HE-1 (induced by source paper "an algebraic geometric approach for linear regression without correspondences"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "dual principal component pursuit"
    - "filtrated algebraic subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "sampling rate distortion"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "near optimal sensor placement for linear inverse problems"
    - "dass distributed adaptive sparse sensing"
    - "intersensor collaboration in distributed quantization networks"
    - "lossy computing of correlated sources with fractional sampling"
    - "optimal phase transitions in compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing with structured sparsity and structured acquisition"):
  with members:
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on minimal trajectories for mobile sampling of bandlimited fields"
    - "sampling and reconstruction of spatial fields using mobile sensors"
    - "exact recovery conditions for sparse representations with partial support information"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "spread spectrum magnetic resonance imaging"
    - "performance bounds for grouped incoherent measurements in compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "unlabeled sensing with random linear measurements"):
  with members:
    - "where you are is who you are user identification by matching statistics"
    - "sampling and reconstruction of spatial fields using mobile sensors"

Connected through hyperedge HE-5 (induced by source paper "multidimensional manhattan sampling and reconstruction"):
  with members:
    - "sampling and reconstruction of spatial fields using mobile sensors"

Connected through hyperedge HE-6 (induced by source paper "on minimal trajectories for mobile sampling of bandlimited fields"):
  with members:
    - "sampling and reconstruction of spatial fields using mobile sensors"

Connected through hyperedge HE-7 (induced by source paper "sampling and reconstruction of spatial fields using mobile sensors"):
  with members:
    - "high resolution distributed sampling of bandlimited fields with low precision sensors"

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.IT(Information Theory)