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: "optimal quantization for compressive sensing under message passing reconstruction"
  Abstract: We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.

Connected through hyperedge HE-1 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "information theoretic thresholds from the cavity method"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "local statistics semidefinite programming and community detection"):
  with members:
    - "a tight degree 4 sum of squares lower bound for the sherrington kirkpatrick hamiltonian"
    - "vector colorings of random ramanujan and large girth irregular graphs"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "learning from untrusted data"
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "optimizing quantization for lasso recovery"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "quantization and compressive sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "message passing de quantization with applications to compressed sensing"
    - "frame permutation quantization"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-4 (induced by source paper "quantization and compressive sensing"):
  with members:
    - "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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"

Connected through hyperedge HE-5 (induced by source paper "distributed quantization for compressed sensing"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "distributed functional scalar quantization simplified"
    - "analysis by synthesis quantization for compressed sensing measurements"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "compressive sensing over networks"
    - "universal rate efficient scalar quantization"
    - "distributed compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "analysis by synthesis based quantization of compressed sensing measurements"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "message passing de quantization with applications to compressed sensing"
    - "projection based and look ahead strategies for atom selection"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "universal rate efficient scalar quantization"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "joint source channel vector quantization for compressed sensing"):
  with members:
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "analysis by synthesis quantization for compressed sensing measurements"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-8 (induced by source paper "distributed quantization for measurement of correlated sparse sources over noisy channels"):
  with members:
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "quantized network coding for correlated sources"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "structured compressed sensing from theory to applications"
    - "distributed compressive sensing"
    - "rate region of the quadratic gaussian two encoder source coding problem"

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)