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: "no downlink pilots are needed in tdd massive mimo"
  Abstract: We consider the Massive Multiple-Input Multiple-Output (MIMO) downlink with maximum-ratio and zero-forcing processing and time-division duplex (TDD) operation. To decode, the terminals must know their instantaneous effective channel gain. Conventionally, it is assumed that by virtue of channel hardening, this instantaneous gain is close to its average and hence that terminals can rely on knowledge of that average (also known as statistical channel information). However, in some propagation environments, such as keyhole channels, channel hardening does not hold. #R##N#We propose a blind algorithm to estimate the effective channel gain at each user, that does not require any downlink pilots. We derive a capacity lower bound of each user for our proposed scheme, applicable to any propagation channel. Compared to the case of no downlink pilots (relying on channel hardening), and compared to training-based estimation using downlink pilots, our blind algorithm performs significantly better. The difference is especially pronounced in environments that do not offer channel hardening.

Connected through hyperedge HE-1 (induced by source paper "massive mimo asymptotics for ray based propagation channels"):
  with members:
    - "mimo channel hardening a physical model based analysis"
    - "channel hardening in massive mimo a measurement based analysis"
    - "energy efficiency and asymptotic performance evaluation of beamforming structures in doubly massive mimo mmwave systems"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "uplink analysis of large mu mimo systems with space constrained arrays in ricean fading"
    - "massive mimo ten myths and one critical question"
    - "asymptotic orthogonality analysis of time domain sparse massive mimo channels"
    - "massive mimo performance evaluation based on measured propagation data"

Connected through hyperedge HE-2 (induced by source paper "learning aided deep path prediction for sphere decoding in large mimo systems"):
  with members:
    - "learning to detect"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation"):
  with members:
    - "massive mimo has unlimited capacity"
    - "multi cell massive mimo performance with double scattering channels"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "random beamforming over quasi static and fading channels a deterministic equivalent approach"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "asymptotic analysis of double scattering channels"

Connected through hyperedge HE-4 (induced by source paper "massive mimo forward link analysis for cellular networks"):
  with members:
    - "massive mimo has unlimited capacity"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "the meta distribution of the sir for cellular networks with power control"
    - "ergodic spectral efficiency in mimo cellular networks"
    - "downlink performance of superimposed pilots in massive mimo systems"
    - "wireless network signals with moderately correlated shadowing still appear poisson"
    - "analyzing uplink sinr and rate in massive mimo systems using stochastic geometry"
    - "the mean interference to signal ratio and its key role in cellular and amorphous networks"

Connected through hyperedge HE-5 (induced by source paper "when machine learning meets wireless cellular networks deployment challenges and applications"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "human centered artificial intelligence and machine learning"
    - "machine learning in the air"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "scalable agent alignment via reward modeling a research direction"
    - "rogue drone detection a machine learning approach"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-6 (induced by source paper "downlink training in cell free massive mimo a blessing in disguise"):
  with members:
    - "ubiquitous cell free massive mimo communications"
    - "on the total energy efficiency of cell free massive mimo"
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "cell free massive mimo versus small cells"
    - "how much do downlink pilots improve cell free massive mimo"
    - "massive mimo ten myths and one critical question"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"

Connected through hyperedge HE-7 (induced by source paper "what role can noma play in massive mimo"):
  with members:
    - "exploiting multiple antenna techniques for non orthogonal multiple access"
    - "performance analysis of noma in training based multiuser mimo systems"
    - "joint power allocation and beamforming for non orthogonal multiple access noma in 5g millimeter wave communications"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "design of massive mimo noma with limited feedback"
    - "aspects of favorable propagation in massive mimo"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-8 (induced by source paper "edge and central cloud computing a perfect pairing for high energy efficiency and low latency"):
  with members:
    - "spectral and energy efficiency of uplink d2d underlaid massive mimo cellular networks"
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "user association in 5g networks a survey and an outlook"
    - "energy efficient resource allocation for mobile edge computing based augmented reality applications"
    - "energy efficient resource allocation for mobile edge computation offloading"

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)