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 "superset technique for approximate recovery in one bit compressed sensing"):
  Abstract: One-bit compressed sensing (1bCS) is a method of signal acquisition under extreme measurement quantization that gives important insights on the limits of signal compression and analog-to-digital conversion. The setting is also equivalent to the problem of learning a sparse hyperplane-classifier. In this paper, we propose a generic approach for signal recovery in nonadaptive 1bCS that leads to improved sample complexity for approximate recovery for a variety of signal models, including nonnegative signals and binary signals. We construct 1bCS matrices that are universal - i.e. work for all signals under a model - and at the same time recover very general random sparse signals with high probability. In our approach, we divide the set of samples (measurements) into two parts, and use the first part to recover the superset of the support of a sparse vector. The second set of measurements is then used to approximate the signal within the superset. While support recovery in 1bCS is well-studied, recovery of superset of the support requires fewer samples, which then leads to an overall reduction in sample complexity for approximate recovery.

Includes members:
  Member "improved bounds for universal one bit compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one bit expandersketch for one bit compressed sensing")
  Member "nonadaptive group testing with random set of defectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "methods for quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "noise resilient group testing limitations and constructions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)