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: "near optimal bounds for binary embeddings of arbitrary sets"
  Abstract: We study embedding a subset $K$ of the unit sphere to the Hamming cube $\{-1,+1\}^m$. We characterize the tradeoff between distortion and sample complexity $m$ in terms of the Gaussian width $\omega(K)$ of the set. For subspaces and several structured sets we show that Gaussian maps provide the optimal tradeoff $m\sim \delta^{-2}\omega^2(K)$, in particular for $\delta$ distortion one needs $m\approx\delta^{-2}{d}$ where $d$ is the subspace dimension. For general sets, we provide sharp characterizations which reduces to $m\approx{\delta^{-4}}{\omega^2(K)}$ after simplification. We provide improved results for local embedding of points that are in close proximity of each other which is related to locality sensitive hashing. We also discuss faster binary embedding where one takes advantage of an initial sketching procedure based on Fast Johnson-Lindenstauss Transform. Finally, we list several numerical observations and discuss open problems.

Connected through hyperedge HE-1 (induced by source paper "one bit sensing of low rank and bisparse matrices"):
  with members:
    - "one bit compressive sensing of dictionary sparse signals"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "taking the edge off quantization projected back projection in dithered compressive sensing"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "isometric sketching of any set via the restricted isometry property"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "the generalized lasso with non linear observations"

Connected through hyperedge HE-4 (induced by source paper "non gaussian hyperplane tessellations and robust one bit compressed sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "bounds on the number of measurements for reliable compressive classification"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "quantization and compressive sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-6 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

Connected through hyperedge HE-7 (induced by source paper "near optimal sample complexity bounds for circulant binary embedding"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "isometric sketching of any set via the restricted isometry property"
    - "binary embedding fundamental limits and fast algorithm"
    - "on binary embedding using circulant matrices"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-8 (induced by source paper "small width low distortions quantized random embeddings of low complexity sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

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.LG(Machine Learning)