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: "compressed sensing with probabilistic measurements a group testing solution"
  Abstract: Detection of defective members of large populations has been widely studied in the statistics community under the name "group testing", a problem which dates back to World War II when it was suggested for syphilis screening. There the main interest is to identify a small number of infected people among a large population using collective samples. In viral epidemics, one way to acquire collective samples is by sending agents inside the population. While in classical group testing, it is assumed that the sampling procedure is fully known to the reconstruction algorithm, in this work we assume that the decoder possesses only partial knowledge about the sampling process. This assumption is justified by observing the fact that in a viral sickness, there is a chance that an agent remains healthy despite having contact with an infected person. Therefore, the reconstruction method has to cope with two different types of uncertainty; namely, identification of the infected population and the partially unknown sampling procedure. #R##N#In this work, by using a natural probabilistic model for "viral infections", we design non-adaptive sampling procedures that allow successful identification of the infected population with overwhelming probability 1-o(1). We propose both probabilistic and explicit design procedures that require a "small" number of agents to single out the infected individuals. More precisely, for a contamination probability p, the number of agents required by the probabilistic and explicit designs for identification of up to k infected members is bounded by m = O(k^2 (log n)/p^2) and m = O(k^2 (log n)^2 /p^2), respectively. In both cases, a simple decoder is able to successfully identify the infected population in time O(mn).

Connected through hyperedge HE-1 (induced by source paper "cross sender bit mixing coding"):
  with members:
    - "non adaptive group testing framework based on concatenation code"
    - "group testing using left and right regular sparse graph codes"
    - "nonadaptive group testing with random set of defectives"
    - "group testing schemes from codes and designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "computing in additive networks with bounded information codes"
    - "group testing algorithms bounds and simulations"
    - "on almost disjunct matrices for group testing"

Connected through hyperedge HE-2 (induced by source paper "local orthogonal group testing"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "fine tuning cnn image retrieval with no human annotation"
    - "learning a complete image indexing pipeline"
    - "deep image retrieval learning global representations for image search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-3 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-4 (induced by source paper "active hypothesis testing on a tree anomaly detection under hierarchical observations"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "semiquantitative group testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "asymptotics of fingerprinting and group testing tight bounds from channel capacities"):
  with members:
    - "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "near optimal stochastic threshold group testing"
    - "dynamic traitor tracing schemes revisited"
    - "the capacity of adaptive group testing"
    - "efficient probabilistic group testing based on traitor tracing"
    - "dynamic tardos traitor tracing schemes"

Connected through hyperedge HE-6 (induced by source paper "efficient probabilistic group testing based on traitor tracing"):
  with members:
    - "optimal symmetric tardos traitor tracing schemes"
    - "near optimal stochastic threshold group testing"
    - "dynamic traitor tracing schemes revisited"
    - "the capacity of adaptive group testing"
    - "dynamic tardos traitor tracing schemes"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"
    - "adaptive group testing as channel coding with feedback"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-7 (induced by source paper "group testing with probabilistic tests theory design and application"):
  with members:
    - "boolean compressed sensing and noisy group testing"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "a deterministic sub linear time sparse fourier algorithm via non adaptive compressed sensing methods"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-8 (induced by source paper "applications of derandomization theory in coding"):
  with members:
    - "graph constrained group testing"
    - "almost uniform sampling of points on high dimensional algebraic varieties"
    - "bit precision analysis for compressed sensing"
    - "noise resilient group testing limitations and constructions"
    - "capacity achieving codes from randomness condensers"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "invertible extractors and wiretap protocols"
    - "on wiretap networks ii"

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)