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: "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case"
  Abstract: Resource allocation is an important issue in cognitive radio systems. It can be done by carrying out negotiation among secondary users. However, significant overhead may be incurred by the negotiation since the negotiation needs to be done frequently due to the rapid change of primary users' activity. In this paper, a channel selection scheme without negotiation is considered for multi-user and multi-channel cognitive radio systems. To avoid collision incurred by non-coordination, each secondary user learns how to select channels according to its experience. Multi-agent reinforcement leaning (MARL) is applied in the framework of Q-learning by considering opponent secondary users as a part of the environment. The dynamics of the Q-learning are illustrated using Metrick-Polak plot. A rigorous proof of the convergence of Q-learning is provided via the similarity between the Q-learning and Robinson-Monro algorithm, as well as the analysis of convergence of the corresponding ordinary differential equation (via Lyapunov function). Examples are illustrated and the performance of learning is evaluated by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"):
  with members:
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "performance analysis of dynamic channel bonding in spatially distributed high density wlans"
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "ieee 802 11ax high efficiency wlans"
    - "multi player bandits a musical chairs approach"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"

Connected through hyperedge HE-3 (induced by source paper "spectrum access in cognitive radio using a two stage reinforcement learning approach"):
  with members:
    - "a brief introduction to machine learning for engineers"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "algorithms for dynamic spectrum access with learning for cognitive radio"

Connected through hyperedge HE-4 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "deep learning for wireless physical layer opportunities and challenges"

Connected through hyperedge HE-5 (induced by source paper "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"):
  with members:
    - "a multi game framework for harmonized lte u and wifi coexistence over unlicensed bands"
    - "opportunistic sleep mode strategies in wireless small cell networks"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "holistic small cell traffic balancing across licensed and unlicensed bands"
    - "modeling and analyzing the coexistence of wi fi and lte in unlicensed spectrum"
    - "dynamic clustering and on off strategies for wireless small cell networks"
    - "why to decouple the uplink and downlink in cellular networks and how to do it"
    - "small cell traffic balancing over licensed and unlicensed bands"

Connected through hyperedge HE-6 (induced by source paper "location privacy in cognitive radio networks a survey"):
  with members:
    - "preserving the location privacy of secondary users in cooperative spectrum sensing"
    - "an efficient technique for protecting location privacy of cooperative spectrum sensing users"
    - "securefind secure and privacy preserving object finding via mobile crowdsourcing"
    - "lpos location privacy for optimal sensing in cognitive radio networks"
    - "geo indistinguishability differential privacy for location based systems"
    - "security of spectrum learning in cognitive radios"
    - "channel assignment with access contention resolution for cognitive radio networks"
    - "towards practical oblivious ram"

Connected through hyperedge HE-7 (induced by source paper "dynamic spectrum access in time varying environment distributed learning beyond expectation optimization"):
  with members:
    - "a game theoretic perspective on self organizing optimization for cognitive small cells"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "adaptive channel allocation spectrum etiquette for cognitive radio networks"

Connected through hyperedge HE-8 (induced by source paper "dynamic spectrum access with statistical qos provisioning a distributed learning approach beyond expectation optimization"):
  with members:
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"
    - "evolutionarily stable spectrum access"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "adaptive channel allocation spectrum etiquette for cognitive radio 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)