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: "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
  Abstract: Recurrent neural networks (RNNs) are state-of-the-art in voice awareness/understanding and speech recognition. On-device computation of RNNs on low-power mobile and wearable devices would be key to applications such as zero-latency voice-based human-machine interfaces. Here we present Chipmunk, a small (<1 mm${}^2$) hardware accelerator for Long-Short Term Memory RNNs in UMC 65 nm technology capable to operate at a measured peak efficiency up to 3.08 Gop/s/mW at 1.24 mW peak power. To implement big RNN models without incurring in huge memory transfer overhead, multiple Chipmunk engines can cooperate to form a single systolic array. In this way, the Chipmunk architecture in a 75 tiles configuration can achieve real-time phoneme extraction on a demanding RNN topology proposed by Graves et al., consuming less than 13 mW of average power.

Connected through hyperedge HE-1 (induced by source paper "long short term memory networks in memristor crossbar arrays"):
  with members:
    - "mixed precision in memory computing"
    - "approximate fpga based lstms under computation time constraints"
    - "the microsoft 2017 conversational speech recognition system"
    - "learning to detect violent videos using convolutional long short term memory"
    - "data recombination for neural semantic parsing"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-2 (induced by source paper "aida associative dnn inference accelerator"):
  with members:
    - "sparse matrix multiplication on an associative processor"
    - "eie efficient inference engine on compressed deep neural network"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "speech recognition with deep recurrent neural networks"
    - "computer architecture with associative processor replacing last level cache and simd accelerator"

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.DC(Distributed, Parallel, and Cluster Computing)