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: "dsp ear leveraging co processor support for continuous audio sensing on smartphones"
  Abstract: The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. #R##N#To address this problem, we propose DSP.Ear - an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naive DSP solution without optimizations. We further analyze a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.

Connected through hyperedge HE-1 (induced by source paper "mobile recognition of wikipedia featured sites using deep learning and crowd sourced imagery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the inaturalist species classification and detection dataset"
    - "using deep learning for image based plant disease detection"
    - "planet photo geolocation with convolutional neural networks"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-2 (induced by source paper "exploring the vision processing unit as co processor for inference"):
  with members:
    - "benchmarking data analysis and machine learning applications on the intel knl many core processor"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "training deep neural networks with low precision multiplications"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving personal model training"):
  with members:
    - "owl a general purpose numerical library in ocaml"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "learning privately from multiparty data"
    - "communication efficient learning of deep networks from decentralized data"
    - "membership inference attacks against machine learning models"
    - "mllib machine learning in apache spark"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-4 (induced by source paper "emotion classification from noisy speech a deep learning approach"):
  with members:
    - "deep speech scaling up end to end speech recognition"
    - "a brief survey on deep belief networks and introducing a new object oriented matlab toolbox deebnet"

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.SD(Sound)