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: "predicting a user s next cell with supervised learning based on channel states"
  Abstract: Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.

Connected through hyperedge HE-1 (induced by source paper "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"):
  with members:
    - "context aware scheduling of joint millimeter wave and microwave resources for dual mode base stations"
    - "anticipatory radio resource management for mobile video streaming with linear programming"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "on characterization of feasible interference regions in cognitive radio networks"
    - "spatial wireless channel prediction under location uncertainty"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "5g cellular key enabling technologies and research challenges"
    - "context aware small cell networks how social metrics improve wireless resource allocation"

Connected through hyperedge HE-2 (induced by source paper "anticipatory networking in future generation mobile networks a survey"):
  with members:
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "spatial wireless channel prediction under location uncertainty"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "predictive green wireless access exploiting mobility and application information"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"
    - "a comparative study of collaborative filtering algorithms"
    - "femtocaching wireless video content delivery through distributed caching helpers"
    - "mobility prediction in wireless ad hoc networks using neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning based resource allocation scheme for tdd based cran system"):
  with members:
    - "joint time domain resource partitioning rate allocation and video quality adaptation in heterogeneous cellular networks"
    - "high dimensional csi acquisition in massive mimo sparsity inspired approaches"
    - "learning based adaptive transmission for limited feedback multiuser mimo ofdm"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-4 (induced by source paper "smarterphones anticipatory download scheduling for wireless video streaming"):
  with members:
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "proactive resource allocation harnessing the diversity and multicast gains"

Connected through hyperedge HE-5 (induced by source paper "energy efficient adaptive video transmission exploiting rate predictions in wireless networks"):
  with members:
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "predictive green wireless access exploiting mobility and application information"
    - "green cellular networks a survey some research issues and challenges"

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.NI(Networking and Internet Architecture)