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: "fault tolerant dot product engines"
  Abstract: Coding schemes are presented that provide the ability to correct and detect computational errors while using dot-product engines for integer vector--matrix multiplication. Both the $L_1$-metric and the Hamming metric are considered.

Connected through hyperedge HE-1 (induced by source paper "puma a programmable ultra efficient memristor based accelerator for machine learning inference"):
  with members:
    - "vibnn hardware acceleration of bayesian neural networks"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-2 (induced by source paper "harnessing intrinsic noise in memristor hopfield neural networks for combinatorial optimization"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "mixed precision in memory computing"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "part detector discovery in deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "short dot computing large linear transforms distributedly using coded short dot products"):
  with members:
    - "codenet training large scale neural networks in presence of soft errors"
    - "speeding up distributed machine learning using codes"
    - "fundamental limits of coded linear transform"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "coded sparse matrix multiplication"
    - "straggler mitigation by delayed relaunch of tasks"

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)