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: "on the complexity of inner product similarity join"
  Abstract: A number of tasks in classification, information retrieval, recommendation systems, and record linkage reduce to the core problem of inner product similarity join (IPS join): identifying pairs of vectors in a collection that have a sufficiently large inner product. IPS join is well understood when vectors are normalized and some approximation of inner products is allowed. However, the general case where vectors may have any length appears much more challenging. Recently, new upper bounds based on asymmetric locality-sensitive hashing (ALSH) and asymmetric embeddings have emerged, but little has been known on the lower bound side. In this paper we initiate a systematic study of inner product similarity join, showing new lower and upper bounds. Our main results are: Approximation hardness of IPS join in subquadratic time, assuming the strong exponential time hypothesis. New upper and lower bounds for (A)LSH-based algorithms. In particular, we show that asymmetry can be avoided by relaxing the LSH definition to only consider the collision probability of distinct elements. A new indexing method for IPS based on linear sketches, implying that our hardness results are not far from being tight.   Our technical contributions include new asymmetric embeddings that may be of independent interest. At the conceptual level we strive to provide greater clarity, for example by distinguishing among signed and unsigned variants of IPS join and shedding new light on the effect of asymmetry.

Connected through hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew"):
  with members:
    - "hardness of bichromatic closest pair with jaccard similarity"
    - "set similarity search for skewed data"
    - "adaptive mapreduce similarity joins"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "lsh ensemble internet scale domain search"
    - "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"

Connected through hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"

Connected through hyperedge HE-3 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"

Connected through hyperedge HE-4 (induced by source paper "the space complexity of inner product filters"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "approximate nearest neighbors in limited space"
    - "adaptive mapreduce similarity joins"
    - "practical data dependent metric compression with provable guarantees"
    - "distributed pcp theorems for hardness of approximation in p"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "bolt accelerated data mining with fast vector compression"
    - "scalable and robust set similarity join"

Connected through hyperedge HE-5 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "fast similarity sketching"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-6 (induced by source paper "on the hardness of approximate and exact bichromatic maximum inner product"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "conditional lower bounds for all pairs max flow"
    - "conditional lower bounds for space time tradeoffs"
    - "distributed pcp theorems for hardness of approximation in p"
    - "on the parameterized complexity of approximating dominating set"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "on the complexity of closest pair via polar pair of point sets"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-7 (induced by source paper "set similarity search for skewed data"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-8 (induced by source paper "an equivalence class for orthogonal vectors"):
  with members:
    - "on problems equivalent to min convolution"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "tight hardness for shortest cycles and paths in sparse graphs"
    - "tight hardness results for maximum weight rectangles"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "strong eth breaks with merlin and arthur short non interactive proofs of batch evaluation"

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.DS(Data Structures and Algorithms)