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: "sparse combinatorial group testing for low energy massive random access"
  Abstract: We present random access schemes for machine-type communication where a massive number of low-energy wireless devices want to occasionally transmit short information packets. We focus on the device discovery problem, with extensions to joint discovery and data transmission as well as data transmission without communicating device identities. We formulate this problem as a combinatorial group testing one, where the goal is to exactly identify the set of at most $d$ defective items from a pool of $n$ items. We translate the energy constraint at the physical layer to a constraint on the number of tests each item can participate in, and study the resulting "sparse" combinatorial group testing problem. #R##N#In our sparse setting, we restrict the number of tests each item can participate in by $w_{\max}$. It is easy to observe that if $w_{\max} \leq d$, then we must have $t=n$; i.e., testing every item individually is optimal. We show that if $w_{\max}=d+1$, the number of tests decreases suddenly from $t=n$ to $t = (d+1)\sqrt{n}$. More generally, if $w_{\max}=ld+1$ for any positive integer $l$ such that $ld+1 \leq \sqrt[l+1]{n}$, we can achieve $t=(ld+1)n^{1/(l+1)}$ using Kautz and Singleton's construction with a particular choice of field size. We also prove a nearly matching lower bound which shows that $t = \Omega(d^{2/l+1}n^{1/(l+1)})$. This shows that in the sparse setting $t$ is a fractional power of $n$, rather than logarithmic in $n$ as in the classical setting. #R##N#Since encoding and decoding efficiency can be just as important as energy efficiency, we demonstrate that our construction can be decoded in (poly$(d) + O(t)$)-time and each entry in any codeword can be computed in space poly$(\log n)$. This shows that our construction not only (nearly) achieves the fundamental lower bound, but also does that with a favorable encoding and decoding complexity.

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)