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: "enabling cooperative inference of deep learning on wearables and smartphones"
  Abstract: Deep Learning (DL) algorithm is the state-of-the-art algorithm of many computer science fields and applied on many intelligent mobile applications. In this paper, we propose a system called CoINF, a practical, adaptive, and flexible deep learning framework that enables cooperative inference between wearable devices (e.g., smartwatches and smart glasses) and handhelds. Our framework accelerates the processing and saves the energy consumption of generic deep learning models inference on wearables via judiciously offloading the workloads to paired handhelds at fine granularity in considering of the system environment, the application requirements, and user preference. Deployed as a user-space library, CoINF offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow, with all complicated offloading details hidden. We have implemented a prototype of CoINF on Android OS, and used real deep learning models to evaluate its performance on commercial off-the-shelf smartphone and smartwatches. The experimental results show that our framework can achieve substantial execution speedup and energy saving compared to wearable-only and handheld-only strategies.

Connected through hyperedge HE-1 (induced by source paper "edge ai on demand accelerating deep neural network inference via edge computing"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "zygarde time sensitive on device deep intelligence on intermittently powered systems"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "musical chair efficient real time recognition using collaborative iot devices"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "efficient resource allocation for on demand mobile edge cloud computing"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "collaborative intelligent cross camera video analytics at edge opportunities and challenges"):
  with members:
    - "scaling video analytics systems to large camera deployments"
    - "designing energy efficient convolutional neural networks using energy aware pruning"

Connected through hyperedge HE-3 (induced by source paper "asynchronous mobile edge computation offloading energy efficient resource management"):
  with members:
    - "latency optimization for resource allocation in mobile edge computation offloading"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "computation rate maximization for wireless powered mobile edge computing with binary computation offloading"
    - "a survey on mobile edge computing the communication perspective"
    - "joint task offloading scheduling and transmit power allocation for mobile edge computing systems"
    - "an online optimization framework for distributed fog network formation with minimal latency"
    - "exploiting non causal cpu state information for energy efficient mobile cooperative computing"
    - "joint computation offloading and resource allocation in cloud based wireless hetnets"

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.CY(Computers and Society)