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: "modeling and energy optimization of ldpc decoder circuits with timing violations"
  Abstract: This paper proposes a "quasi-synchronous" design approach for signal processing circuits, in which timing violations are permitted, but without the need for a hardware compensation mechanism. The case of a low-density parity-check (LDPC) decoder is studied, and a method for accurately modeling the effect of timing violations at a high level of abstraction is presented. The error-correction performance of code ensembles is then evaluated using density evolution while taking into account the effect of timing faults. Following this, several quasi-synchronous LDPC decoder circuits based on the offset min-sum algorithm are optimized, providing a 23%-40% reduction in energy consumption or energy-delay product, while achieving the same performance and occupying the same area as conventional synchronous circuits.

Connected through hyperedge HE-1 (induced by source paper "a study of deep learning robustness against computation failures"):
  with members:
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep speech scaling up end to end speech recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-2 (induced by source paper "vlsi implementation of deep neural network using integral stochastic computing"):
  with members:
    - "successive cancellation decoding of polar codes using stochastic computing"

Connected through hyperedge HE-3 (induced by source paper "energy latency and reliability tradeoffs in coding circuits"):
  with members:
    - "on the total power capacity of regular ldpc codes with iterative message passing decoders"
    - "information theory of molecular communication directions and challenges"
    - "energy harvesting transmitters that heat up throughput maximization under temperature constraints"
    - "on scaling rules for energy of vlsi polar encoders and decoders"
    - "information friction and its implications on minimum energy required for communication"
    - "on the energy complexity of ldpc decoder circuits"
    - "energy efficient decoders for compressive sensing fundamental limits and implementations"
    - "computing linear transformations with unreliable components"

Connected through hyperedge HE-4 (induced by source paper "majority logic decoding under data dependent logic gate failures"):
  with members:
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "state dependent statistical timing model for voltage scaled circuits"
    - "on trapping sets and guaranteed error correction capability of ldpc codes and gldpc codes"
    - "performance of ldpc codes under faulty iterative decoding"

Connected through hyperedge HE-5 (induced by source paper "design of ldpc codes robust to noisy message passing decoding"):
  with members:
    - "channels that heat up"
    - "performance of ldpc codes under faulty iterative decoding"

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)