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: "channel optimized vector quantizer design for compressed sensing measurements"
  Abstract: We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account.

Connected through hyperedge HE-1 (induced by source paper "analysis by synthesis based quantization of compressed sensing measurements"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "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"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "universal rate efficient scalar quantization"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "power constrained sparse gaussian linear dimensionality reduction over noisy channels"):
  with members:
    - "joint source channel vector quantization for compressed sensing"
    - "estimating unknown sparsity in compressed sensing"
    - "projection based and look ahead strategies for atom selection"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "on mmse and map denoising under sparse representation modeling over a unitary dictionary"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "joint source channel vector quantization for compressed sensing"):
  with members:
    - "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"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "distributed quantization for measurement of correlated sparse sources over noisy channels"):
  with members:
    - "quantized network coding for correlated sources"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "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)