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: "mobile big data analytics using deep learning and apache spark"
  Abstract: The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

Connected through hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach"):
  with members:
    - "hybrid pricing for mobile collaborative internet access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "context aware computing for the internet of things a survey"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "a theory of pricing private data"
    - "a survey of smart data pricing past proposals current plans and future trends"

Connected through hyperedge HE-2 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-3 (induced by source paper "structured bayesian compression for deep models in mobile enabled devices for connected healthcare"):
  with members:
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-4 (induced by source paper "big data meet cyber physical systems a panoramic survey"):
  with members:
    - "information and communications technologies for sustainable development goals state of the art needs and perspectives"
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "developing an edge computing platform for real time descriptive analytics"
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "wireless device to device caching networks basic principles and system performance"
    - "social computing for mobile big data"
    - "streaming big data meets backpressure in distributed network computation"
    - "big data caching for networking moving from cloud to edge"

Connected through hyperedge HE-5 (induced by source paper "mobile big data analysis with machine learning"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "distributed optimization using the primal dual method of multipliers"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "the role of data analysis in the development of intelligent energy networks"
    - "big data caching for networking moving from cloud to edge"
    - "soft defined heterogeneous vehicular network architecture and challenges"
    - "5g wireless backhaul networks challenges and research advances"

Connected through hyperedge HE-6 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "applying neural networks in optical communication systems possible pitfalls"

Connected through hyperedge HE-7 (induced by source paper "distributed bayesian piecewise sparse linear models"):
  with members:
    - "rebuilding factorized information criterion asymptotically accurate marginal likelihood"
    - "alid scalable dominant cluster detection"
    - "mli an api for distributed machine learning"
    - "factorized asymptotic bayesian hidden markov models"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-8 (induced by source paper "fast adaptation of activity sensing policies in mobile devices"):
  with members:
    - "context aware computing for the internet of things a survey"
    - "on the complexity of solving markov decision problems"

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)