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: "quantization of binary input discrete memoryless channels"
  Abstract: The quantization of the output of a binary-input discrete memoryless channel to a smaller number of levels is considered. An algorithm which finds an optimal quantizer, in the sense of maximizing mutual information between the channel input and the quantizer output is given. This result holds for arbitrary channels, in contrast to previous results for restricted channels or a restricted number of quantizer outputs. In the worst case, the algorithm complexity is cubic $M^3$ in the number of channel outputs $M$. Optimality is proved using the theorem of Burshtein, Della Pietra, Kanevsky, and N\'adas for mappings which minimize average impurity for classification and regression trees.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "entropy constrained maximizing mutual information quantization"):
  with members:
    - "dynamic programming for discrete memoryless channel quantization"
    - "minimizing impurity partition under constraints"
    - "at low snr asymmetric quantizers are better"
    - "how to construct polar codes"

Connected through hyperedge HE-3 (induced by source paper "categorical feature compression via submodular optimization"):
  with members:
    - "bounds on the entropy of a function of a random variable and their applications"
    - "tensorflow a system for large scale machine learning"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "adam a method for stochastic optimization"
    - "lazier than lazy greedy"

Connected through hyperedge HE-4 (induced by source paper "deep log likelihood ratio quantization"):
  with members:
    - "deep learning based quantization of l values for gray coded modulation"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "deep learning in mobile and wireless networking a survey"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "dynamic programming for sequential deterministic quantization of discrete memoryless channels"):
  with members:
    - "mutual information maximizing quantized belief propagation decoding of ldpc codes"
    - "information theoretic bounds based channel quantization design for emerging memories"
    - "enhanced precision through multiple reads for ldpc decoding in flash memories"
    - "how to construct polar codes"

Connected through hyperedge HE-6 (induced by source paper "dynamic programming for discrete memoryless channel quantization"):
  with members:
    - "mutual information maximizing quantized belief propagation decoding of ldpc codes"
    - "information theoretic bounds based channel quantization design for emerging memories"
    - "how to construct polar codes"

Connected through hyperedge HE-7 (induced by source paper "mutual information maximizing quantized belief propagation decoding of ldpc codes"):
  with members:
    - "dynamic programming for discrete memoryless channel quantization"
    - "quantized message passing for ldpc codes"
    - "finite alphabet iterative decoders part ii towards guaranteed error correction of ldpc codes via iterative decoder diversity"

Connected through hyperedge HE-8 (induced by source paper "q search trees an information theoretic approach towards hierarchical abstractions for agents with computational limitations"):
  with members:
    - "hierarchical state abstractions for decision making problems with computational constraints"
    - "information utility bounded rationality"

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)