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: "user association for load balancing in heterogeneous cellular networks"
  Abstract: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rates for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A_j>=1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide optimal association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A_j are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a max received power association.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "random caching based cooperative transmission in heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "network slicing games enabling customization in multi tenant networks"

Connected through hyperedge HE-3 (induced by source paper "secrecy outage analysis of non orthogonal spectrum sharing for heterogeneous cellular networks"):
  with members:
    - "security reliability tradeoff for distributed antenna systems in heterogeneous cellular networks"
    - "intelligent interference exploitation for heterogeneous cellular networks against eavesdropping"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "a new look at physical layer security caching and wireless energy harvesting for heterogeneous ultra dense networks"
    - "physical layer security for spectrum sharing systems"
    - "artificial noise aided secure transmission in large scale spectrum sharing networks"
    - "location based beamforming and physical layer security in rician wiretap channels"
    - "physical layer security in millimeter wave cellular networks"

Connected through hyperedge HE-4 (induced by source paper "an efficient requirement aware attachment policy for future millimeter wave vehicular networks"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "toward intelligent vehicular networks a machine learning framework"
    - "user association in massive mimo hetnets"
    - "hybrid spectrum sharing in mmwave cellular networks"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "initial access in 5g mmwave cellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-5 (induced by source paper "space diversity based grant free random access for critical and non critical iot services"):
  with members:
    - "multiple relay slotted aloha performance analysis and bounds"
    - "modern random access for beyond 5g systems a multiple relay aloha perspective"
    - "information centric grant free access for iot fog networks edge vs cloud detection and learning"
    - "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "grant free radio access for short packet communications over 5g networks"
    - "grant free radio access iot networks scalability analysis in coexistence scenarios"
    - "two user erasure interference channels with local delayed csit"

Connected through hyperedge HE-6 (induced by source paper "dynamic spectrum sharing for load balancing in multi cell mobile edge computing"):
  with members:
    - "joint service placement and request routing in multi cell mobile edge computing networks"
    - "mobile edge computing enabled heterogeneous networks"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "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-7 (induced by source paper "user association in dense mmwave networks based on rate requirements"):
  with members:
    - "time fractional user association in millimeter wave mimo networks"
    - "maximum throughput scheduling for multi connectivity in millimeter wave networks"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "optimal user cell association for massive mimo wireless networks"
    - "what will 5g be"

Connected through hyperedge HE-8 (induced by source paper "joint user association and resource allocation in heterogeneous cellular networks comparison of two modeling approaches"):
  with members:
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"
    - "data offloading in load coupled networks a utility maximization framework"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "automated dynamic offset applied to cell association"

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)