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: "sequential adaptive compressed sampling via huffman codes"
  Abstract: There are two main approaches in compressed sensing: the geometric approach and the combinatorial approach. In this paper we introduce an information theoretic approach and use results from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a sparse signal. Unlike other approaches, our approach is adaptive in the sense that each sampling vector depends on the previous sample. The number of measurements we need for a k-sparse vector in n-dimensional space is no more than O(k log n) and the reconstruction is O(k).

Connected through hyperedge HE-1 (induced by source paper "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "welfare maximization with limited interaction"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "lazier than lazy greedy"

Connected through hyperedge HE-2 (induced by source paper "improved algorithms for adaptive compressed sensing"):
  with members:
    - "sublinear time algorithms for compressive phase retrieval"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "lower bounds for adaptive sparse recovery"
    - "on the power of adaptivity in sparse recovery"
    - "1 eps approximate sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for adaptive sparse recovery"):
  with members:
    - "nearly optimal sparse fourier transform"
    - "information equals amortized communication"
    - "on the power of adaptivity in sparse recovery"
    - "how well can we estimate a sparse vector"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-4 (induced by source paper "on the power of adaptivity in sparse recovery"):
  with members:
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "fast moment estimation in data streams in optimal space"
    - "approximate sparse recovery optimizing time and measurements"

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)