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 "learning erd h o s r enyi random graphs via edge detecting queries"):
  Abstract: In this paper, we consider the problem of learning an unknown graph via queries on groups of nodes, with the result indicating whether or not at least one edge is present among those nodes. While learning arbitrary graphs with $n$ nodes and $k$ edges is known to be hard the sense of requiring $\Omega( \min\{ k^2 \log n, n^2\})$ tests (even when a small probability of error is allowed), we show that learning an Erdős-Renyi random graph with an average of $\bar{k}$ edges is much easier; namely, one can attain asymptotically vanishing error probability with only $O(\bar{k} \log n)$ tests. We establish such bounds for a variety of algorithms inspired by the group testing problem, with explicit constant factors indicating a near-optimal number of tests, and in some cases asymptotic optimality including constant factors. In addition, we present an alternative design that permits a near-optimal sublinear decoding time of $O(\bar{k} \log^2 \bar{k} + \bar{k} \log n)$.

Includes members:
  Member "an introductory guide to fano s inequality with applications in statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning adversarially robust representations via worst case mutual information maximization")
  Member "group testing an information theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "performance of group testing algorithms with near constant tests per item":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "information theoretic and algorithmic thresholds for group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "on the optimality of the kautz singleton construction in probabilistic group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding")
  Member "on learning graphs with edge detecting queries":
    also connected through other hyperedges:
      (none)
  Member "noisy non adaptive group testing a near definite defectives approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the optimality of the kautz singleton construction in probabilistic group testing")
  Member "near optimal noisy group testing via separate decoding of items":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the all or nothing behavior of bernoulli group testing")

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.IT(Information Theory)