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: "compressing sets and multisets of sequences"
  Abstract: This article describes lossless compression algorithms for multisets of sequences, taking advantage of the multiset's unordered structure. Multisets are a generalisation of sets where members are allowed to occur multiple times. A multiset can be encoded na\"ively by simply storing its elements in some sequential order, but then information is wasted on the ordering. We propose a technique that transforms the multiset into an order-invariant tree representation, and derive an arithmetic code that optimally compresses the tree. Our method achieves compression even if the sequences in the multiset are individually incompressible (such as cryptographic hash sums). The algorithm is demonstrated practically by compressing collections of SHA-1 hash sums, and multisets of arbitrary, individually encodable objects.

Connected through hyperedge HE-1 (induced by source paper "loads of space local order agnosticism and bit flip efficient data structure codes"):
  with members:
    - "codes in the space of multisets coding for permutation channels with impairments"

Connected through hyperedge HE-2 (induced by source paper "universal and succinct source coding of deep neural networks"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "learning compact recurrent neural networks"
    - "flow of information in feed forward deep neural networks"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "compressing neural networks with the hashing trick"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

Connected through hyperedge HE-3 (induced by source paper "codes in the space of multisets coding for permutation channels with impairments"):
  with members:
    - "fundamental limits of dna storage systems"
    - "duplication correcting codes for data storage in the dna of living organisms"
    - "perfect codes in the discrete simplex"
    - "multiset codes for permutation channels"
    - "subset codes for packet networks"
    - "codes in permutations and error correction for rank modulation"

Connected through hyperedge HE-4 (induced by source paper "rate distortion theory of finite point processes"):
  with members:
    - "the shannon lower bound is asymptotically tight"
    - "covering point patterns"
    - "benefiting from disorder source coding for unordered data"

Connected through hyperedge HE-5 (induced by source paper "coding of graphs with application to graph anomaly detection"):
  with members:
    - "graph based anomaly detection and description a survey"

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)