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: "interference constrained pricing for d2d networks"
  Abstract: The concept of device-to-device (D2D) communications underlaying cellular networks opens up potential benefits for improving system performance but also brings new challenges such as interference management. In this paper, we propose a pricing framework for interference management from the D2D users to the cellular system, where the base station (BS) protects itself (or its serving cellular users) by pricing the crosstier interference caused from the D2D users. A Stackelberg game is formulated to model the interactions between the BS and D2D users. Specifically, the BS sets prices to a maximize its revenue (or any desired utility) subject to an interference temperature constraint. For given prices, the D2D users competitively adapt their power allocation strategies for individual utility maximization. We first analyze the competition among the D2D users by noncooperative game theory and an iterative based distributed power allocation algorithm is proposed. Then, depending on how much network information the BS knows, we develop two optimal algorithms, one for uniform pricing with limited network information and the other for differentiated pricing with global network information. The uniform pricing algorithm can be implemented by a fully distributed manner and requires minimum information exchange between the BS and D2D users, and the differentiated pricing algorithm is partially distributed and requires no iteration between the BS and D2D users. Then a suboptimal differentiated pricing scheme is proposed to reduce complexity and it can be implemented in a fully distributed fashion. Extensive simulations are conducted to verify the proposed framework and algorithms.

Connected through hyperedge HE-1 (induced by source paper "a crowdsourcing framework for on device federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "online job scheduling in distributed machine learning clusters"
    - "federated optimization for heterogeneous networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-2 (induced by source paper "price based distributed offloading for mobile edge computing with computation capacity constraints"):
  with members:
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-3 (induced by source paper "optimal time reuse in cooperative d2d relaying networks"):
  with members:
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "coexistence of wi fi and heterogeneous small cell networks sharing unlicensed spectrum"
    - "to relay or not to relay learning device to device relaying strategies in cellular networks"

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.IT(Information Theory)