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: "alpaca intermittent execution without checkpoints"
  Abstract: The emergence of energy harvesting devices creates the potential for batteryless sensing and computing devices. Such devices operate only intermittently, as energy is available, presenting a number of challenges for software developers. Programmers face a complex design space requiring reasoning about energy, memory consistency, and forward progress. This paper introduces Alpaca, a low-overhead programming model for intermittent computing on energy-harvesting devices. Alpaca programs are composed of a sequence of user-defined tasks. The Alpaca runtime preserves execution progress at the granularity of a task. The key insight in Alpaca is the privatization of data shared between tasks. Updates of shared values in a task are privatized and only committed to main memory on successful execution of the task, ensuring that data remain consistent despite power failures. Alpaca provides a familiar programming interface and a highly efficient runtime model. We also present an alternate version of Alpaca, Alpaca-undo, that uses undo-logging and rollback instead of privatization and commit. We implemented a prototype of both versions of Alpaca as an extension to C with an LLVM compiler pass. We evaluated Alpaca, and directly compared to three systems from prior work. Alpacaconsistently improves performance compared to the previous systems, by up to 23.8x, while also improving memory footprint in many cases, by up to 17.6x.

Connected through hyperedge HE-1 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  with members:
    - "rethinking floating point for deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "adaptive neural networks for efficient inference"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "intermittent learning on device machine learning on intermittently powered system"):
  with members:
    - "intelligence beyond the edge inference on intermittent embedded systems"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "lightnn filling the gap between conventional deep neural networks and binarized networks"
    - "sparse neural networks topologies"
    - "dcnns on a diet sampling strategies for reducing the training set size"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-3 (induced by source paper "efficient lock free durable sets"):
  with members:
    - "fine grain checkpointing with in cache line logging"
    - "delay free concurrency on faulty persistent memory"
    - "reclaiming memory for lock free data structures there has to be a better way"
    - "efficient logging in non volatile memory by exploiting coherency protocols"

Connected through hyperedge HE-4 (induced by source paper "a machine learning accelerator in memory for energy harvesting"):
  with members:
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-5 (induced by source paper "secucode intrinsic puf entangled secure wireless code dissemination for computational rfid devices"):
  with members:
    - "wisent robust downstream communication and storage for computational rfids"

Connected through hyperedge HE-6 (induced by source paper "intelligence beyond the edge inference on intermittent embedded systems"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "lightnn filling the gap between conventional deep neural networks and binarized networks"
    - "binarized convolutional neural networks with separable filters for efficient hardware acceleration"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-7 (induced by source paper "the parallel persistent memory model"):
  with members:
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "sorting with asymmetric read and write costs"
    - "efficient algorithms with asymmetric read and write costs"
    - "lock free concurrent data structures"

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)