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"
  Abstract: In combinatorial group testing (CGT), the objective is to identify the set of at most $d$ defective items from a pool of $n$ items using as few tests as possible. The celebrated result for the CGT problem is that the number of tests $t$ can be made logarithmic in $n$ when $d=O(poly(\log n))$. However, state-of-the-art GT codes require the items to be tested $w=\Omega(d\log n)$ times and tests to include $\rho=\Omega(n/d)$ items (within log factors). In many applications, items can only participate in a limited number of tests and tests are constrained to include a limited number of items. #R##N#In this paper, we study the "sparse" regime for the group testing problem where we restrict the number of tests each item can participate in by $w_{\max}$ or the number of items each test can include by $\rho_{\max}$ in both noiseless and noisy settings. These constraints lead to an unexplored regime where $t$ is a fractional power of $n$. Our results characterize the number of tests $t$ as a function of $w_{\max} (\rho_{\max})$ and show, for example, that $t$ decreases drastically when $w_{\max}$ is increased beyond a bare minimum. In particular, if $w_{\max}\leq d$, then we must have $t=n$, i.e., individual testing is optimal. We show that if $w_{\max}=d+1$, this decreases suddenly to $t=\Theta(d\sqrt{n})$. The order-optimal construction is obtained via a modification of the Kautz-Singleton construction, which is known to be suboptimal for the classical GT problem. For more general case, when $w_{\max}=ld+1$ for $l>1$, the modified K-S construction requires $t=\Theta(d n^{\frac{1}{l+1}})$ tests, which we prove to be near order-optimal. We show that our constructions have a favorable encoding and decoding complexity. We finally discuss an application of our results to the construction of energy-limited random access schemes for IoT networks, which provided the initial motivation for our work.

Connected through hyperedge HE-1 (induced by source paper "group testing with runlength constraints for topological molecular storage"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "non adaptive quantitative group testing using irregular sparse graph codes"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "nearly optimal sparse group testing"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-2 (induced by source paper "massive access for future wireless communication systems"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the fundamental limits of mimo massive access communication"
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "energy efficient coded random access for the wireless uplink"
    - "sparcs for unsourced random access"
    - "random access channel coding in the finite blocklength regime"

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)