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: "recognizing detailed human context in the wild from smartphones and smartwatches"
  Abstract: We demonstrate usage of smartphones and smartwatches to automatically recognize a person's behavioral context in-the-wild. In our setup, subjects use their own personal phones and engage in regular behavior in their natural environments. Our system fuses complementary information from multi-modal sensors and simultaneously recognizes many contextual attributes from diverse behavioral domains, including work and leisure activities, body movement, transportation, and more. Our large scale labeled dataset is publicly available.

Connected through hyperedge HE-1 (induced by source paper "benchmarking tinyml systems challenges and direction"):
  with members:
    - "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "mlperf inference benchmark"
    - "haq hardware aware automated quantization with mixed precision"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "hello edge keyword spotting on microcontrollers"

Connected through hyperedge HE-2 (induced by source paper "federated multi task hierarchical attention model for sensor analytics"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "federated learning strategies for improving communication efficiency"
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "qubithd a stochastic acceleration method for hd computing based machine learning"):
  with members:
    - "dank learning generating memes using deep neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (induced by source paper "animo sharing biosignals on a smartwatch for lightweight social connection"):
  with members:
    - "breeze sharing biofeedback through wearable technologies"

Connected through hyperedge HE-5 (induced by source paper "learning behavioral context recognition with multi stream temporal convolutional networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "depthwise separable convolutions for neural machine translation"
    - "xception deep learning with depthwise separable convolutions"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "deep activity recognition models with triaxial accelerometers"

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.AI(Artificial Intelligence)