human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "coding with asymmetric prior knowledge"):
  Abstract: We study the problem of compressed sensing with asymmetric prior information, as motivated by networking applications such as identity tracking in an Internet-of-Things environment. We focus on the scenario in which a resource-limited encoder needs to report a small subset $S$ from a universe of $N$ objects to a more powerful decoder. The distinguishing feature of our model is asymmetry: the subset $S$ is an i.i.d. sample from a prior distribution $\mu$, and $\mu$ is only known to the decoder. This scenario implies that the encoder must use an oblivious compression scheme which can, nonetheless, achieve communication comparable to the entropy rate $|S| \cdot H(\mu)$, the standard benchmark when both encoder and decoder have access to the prior $\mu$ (as achieved by the Huffman code). #R##N#We first show that in order to exploit the prior $\mu$ in a non-trivial way in such asymmetric information scenario, the compression scheme must be randomized. This stands in contrast to the symmetric case (when both the encoder and decoder know $\mu$), where the Huffman code provides a near-optimal deterministic solution. On the other hand, a rather simple argument shows that, when $|S|=k$, a random linear code achieves essentially optimal communication rate of $O(k\cdot H(\mu))$ bits, nearly-matching the communication benchmark in the symmetric case. Alas, the resulting scheme has prohibitive decoding time: about ${N\choose k} \approx (N/k)^k$. #R##N#Our main result is a computationally efficient and linear coding scheme, which achieves an $O(\lg\lg N)$-competitive communication ratio compared to the optimal benchmark, and runs in $\text{poly}(N,k)$ time. Our "multi-level" coding scheme uses a combination of hashing and syndrome-decoding of Reed-Solomon codes, and relies on viewing the (unknown) prior $\mu$ as a rather small convex combination of uniform ("flat") distributions.

Includes members:
  Member "maximizing broadcast throughput under ultra low power constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on optimal neighbor discovery")
  Member "robust bayesian compressed sensing over finite fields asymptotic performance analysis":
    also connected through other hyperedges:
      (none)
  Member "universal polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy preserving learning via deep net pruning")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)