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: "fully distributed optimal channel assignment for open spectrum access"
  Abstract: In this paper, we address the problem of fully distributed assignment of users to sub-bands such that the sum-rate of the system is maximized. We introduce a modified auction algorithm that can be applied in a fully distributed way using an opportunistic CSMA assignment scheme and is e optimal. We analyze the expected time complexity of the algorithm and suggest a variant to the algorithm that has lower expected complexity. We then show that, in the case of i.i.d. Rayleigh channels, a simple greedy scheme is asymptotically optimal as SNR increases or as the number of users is increased to infinity. We conclude by providing simulated results of the suggested algorithms.

Connected through hyperedge HE-1 (induced by source paper "distributed learning for channel allocation over a shared spectrum"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "game of thrones fully distributed learning for multi player bandits"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-2 (induced by source paper "on the needs for maas platforms to handle competition in ridesharing mobility"):
  with members:
    - "a dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers"
    - "a framework to integrate mode choice in the design of mobility on demand systems"
    - "the impact of ridesharing in mobility on demand systems simulation case study in prague"
    - "the price of fragmentation in mobility on demand services"

Connected through hyperedge HE-3 (induced by source paper "asymptotically optimal resource block allocation with limited feedback"):
  with members:
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "asymptotically optimal distributed channel allocation a competitive game theoretic approach"

Connected through hyperedge HE-4 (induced by source paper "game of thrones fully distributed learning for multi player bandits"):
  with members:
    - "distributed learning for channel allocation over a shared spectrum"
    - "coordinated exploration in concurrent reinforcement learning"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "the effect of communication on noncooperative multiplayer multi armed bandit problems"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "multi user lax communications a multi armed bandit approach"

Connected through hyperedge HE-5 (induced by source paper "energy efficiency of opportunistic device to device relaying under lognormal shadowing"):
  with members:
    - "dore an experimental framework to enable outband d2d relay in cellular networks"
    - "a stochastic geometric analysis of device to device communications operating over generalized fading channels"
    - "device relaying in cellular d2d networks a fairness perspective"
    - "effects of relay selection strategies on the spectral efficiency of wireless systems with half and full duplex nodes"
    - "a survey on device to device communication in cellular networks"
    - "energy efficiency optimization in relay assisted mimo systems with perfect and statistical csi"
    - "what will 5g be"
    - "achieving maximum energy efficiency in multi relay ofdma cellular networks a fractional programming approach"

Connected through hyperedge HE-6 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "approximate best response dynamics in random interference games"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"
    - "active anomaly detection in heterogeneous processes"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "game theoretic dynamic channel allocation for frequency selective interference channels"):
  with members:
    - "approximate best response dynamics in random interference games"
    - "asymptotically optimal distributed channel allocation a competitive game theoretic approach"
    - "on the nash equilibria in decentralized parallel interference channels"
    - "learning equilibria with partial information in decentralized wireless networks"
    - "shannon meets nash on the interference channel"
    - "game theory and the frequency selective interference channel"
    - "the mimo iterative waterfilling algorithm"
    - "competitive design of multiuser mimo systems based on game theory a unified view"

Connected through hyperedge HE-8 (induced by source paper "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"):
  with members:
    - "a context aware matching game for user association in wireless small cell networks"
    - "distributed medium access control for energy efficient transmission in cognitive radios"
    - "joint sensing and power allocation in nonconvex cognitive radio games nash equilibria and distributed algorithms"
    - "exchange economy in two user multiple input single output interference channels"

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)