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: "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
  Abstract: For every fixed constant $\alpha > 0$, we design an algorithm for computing the $k$-sparse Walsh-Hadamard transform of an $N$-dimensional vector $x \in \mathbb{R}^N$ in time $k^{1+\alpha} (\log N)^{O(1)}$. Specifically, the algorithm is given query access to $x$ and computes a $k$-sparse $\tilde{x} \in \mathbb{R}^N$ satisfying $\|\tilde{x} - \hat{x}\|_1 \leq c \|\hat{x} - H_k(\hat{x})\|_1$, for an absolute constant $c > 0$, where $\hat{x}$ is the transform of $x$ and $H_k(\hat{x})$ is its best $k$-sparse approximation. Our algorithm is fully deterministic and only uses non-adaptive queries to $x$ (i.e., all queries are determined and performed in parallel when the algorithm starts). #R##N#An important technical tool that we use is a construction of nearly optimal and linear lossless condensers which is a careful instantiation of the GUV condenser (Guruswami, Umans, Vadhan, JACM 2009). Moreover, we design a deterministic and non-adaptive $\ell_1/\ell_1$ compressed sensing scheme based on general lossless condensers that is equipped with a fast reconstruction algorithm running in time $k^{1+\alpha} (\log N)^{O(1)}$ (for the GUV-based condenser) and is of independent interest. Our scheme significantly simplifies and improves an earlier expander-based construction due to Berinde, Gilbert, Indyk, Karloff, Strauss (Allerton 2008). #R##N#Our methods use linear lossless condensers in a black box fashion; therefore, any future improvement on explicit constructions of such condensers would immediately translate to improved parameters in our framework (potentially leading to $k (\log N)^{O(1)}$ reconstruction time with a reduced exponent in the poly-logarithmic factor, and eliminating the extra parameter $\alpha$). #R##N#Finally, by allowing the algorithm to use randomness, while still using non-adaptive queries, the running time of the algorithm can be improved to $\tilde{O}(k \log^3 N)$.

Connected through hyperedge HE-1 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "simple codes and sparse recovery with fast decoding"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "cross sender bit mixing coding"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"):
  with members:
    - "sparse reconstruction from hadamard matrices a lower bound"
    - "estimating the frequency of a clustered signal"
    - "improved lower bounds for the restricted isometry property of subsampled fourier matrices"
    - "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "active regression via linear sample sparsification"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-3 (induced by source paper "deterministic sparse fourier transform with an ell_infty guarantee"):
  with members:
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "deterministic heavy hitters with sublinear query time"
    - "an adaptive sublinear time block sparse fourier transform"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"

Connected through hyperedge HE-4 (induced by source paper "reconstruction under outliers for fourier sparse functions"):
  with members:
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "efficient algorithms for outlier robust regression"
    - "robust polynomial regression up to the information theoretic limit"
    - "fourier sparse interpolation without a frequency gap"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"

Connected through hyperedge HE-5 (induced by source paper "simple codes and sparse recovery with fast decoding"):
  with members:
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "for all sparse recovery in near optimal time"
    - "noise resilient group testing limitations and constructions"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "explicit non adaptive combinatorial group testing schemes"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-6 (induced by source paper "spright a fast and robust framework for sparse walsh hadamard transform"):
  with members:
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "a fast hadamard transform for signals with sub linear sparsity in the transform domain"
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-7 (induced by source paper "for all sparse recovery in near optimal time"):
  with members:
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "sublinear time measurement optimal sparse recovery for all"
    - "noise resilient group testing limitations and constructions"
    - "approximate sparse recovery optimizing time and measurements"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

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)