human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "aida associative dnn inference accelerator"):
  Abstract: We propose AIDA, an inference engine for accelerating fully-connected (FC) layers of Deep Neural Network (DNN). AIDA is an associative in-memory processor, where the bulk of data never leaves the confines of the memory arrays, and processing is performed in-situ. AIDA area and energy efficiency strongly benefit from sparsity and lower arithmetic precision. We show that AIDA outperforms the state of art inference accelerator, EIE, by 14.5x (peak performance) and 2.5x (throughput).

Includes members:
  Member "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "long short term memory networks in memristor crossbar arrays")
  Member "sparse matrix multiplication on an associative processor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prins resistive cam processing in storage")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "convolutional networks for fast energy efficient neuromorphic computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "computer architecture with associative processor replacing last level cache and simd accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prins resistive cam processing in storage")

Question: Please tell me which class the source paper 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.DC(Distributed, Parallel, and Cluster Computing)